Abstract: The present disclosure provides a shielding apparatus for a lower end of an injection needle and an injection device, and relates to the technical field of injection instruments. By using the shielding apparatus for a lower end of an injection needle in the present disclosure, in the process of mounting a needle holder on an insulin pen, and when the injection needle is completely removed from the insulin pen, the limit block also moves downwardly to abut against a bottom wall of the moving groove, and the shielding cylinder completely covers the lower end of the injection needle, so as to avoid a situation that a user accidentally touches the lower end of an injection needle to be injured, thereby avoiding the safety risk when the lower end of the injection needle is removed.

Abstract: The present disclosure provides a safe puncture needle, and relates to the technical field of medical devices, which comprises a needle holder, a base and a needle tubing, wherein the needle tubing comprises a needle tubing body and a needle tubing tip having a bent structure, the base comprises a base body and a guide block, the base body is provided with a second inner space, the base body is provided with a first through-hole and a second through-hole communicating with the second inner space, the guide block is provided with a third through-hole, the needle tubing body passes through the first through-hole and the third through-hole and is slidably connected to the guide block, and the needle tubing tip retracts from the second through-hole.

Abstract: A method includes obtaining raw image data, where the raw image data includes data values each having most significant bits and least significant bits. The method also includes providing the raw image data to a trained machine learning model and generating processed image data using the trained machine learning model. The method further includes presenting an image based on the processed image data. The trained machine learning model is trained to modulate a feature map associated with the most significant bits of the data values of the raw image data based on the least significant bits of the data values of the raw image data in order to generate a fusion of the most significant bits and the least significant bits of the data values of the raw image data.

Abstract: A method includes obtaining, using a first electronic device, a weight matrix associated with a trained transformer model. The method also includes factorizing the weight matrix into a dictionary weight matrix and an intermediate matrix. The method further includes pruning the intermediate matrix to generate a sparse intermediate matrix. The method also includes fine-tuning the sparse intermediate matrix based on a training dataset to generate a fine-tuned sparse intermediate matrix. The method further includes determining an index matrix and a coefficient matrix based on the fine-tuned sparse intermediate matrix. In addition, the method includes deploying the dictionary weight matrix, the index matrix, and the coefficient matrix to a second electronic device without deploying the weight matrix to the second electronic device. A number of parameters in the dictionary weight matrix, the index matrix, and the coefficient matrix is smaller than a number of parameters in the weight matrix.

Abstract: A method includes obtaining a parameter matrix associated with a linear layer of a first machine learning model and containing parameter values for parameters of the linear layer. The method also includes determining importance values corresponding to the parameter values. The method further includes generating factorized matrices such that a product of the importance values and factorized matrices contains approximated parameter values for the parameters of the linear layer. In addition, the method includes generating a second machine learning model representing a compressed version of the first machine learning model. The second machine learning model has first and second linear layers containing parameter values based on the importance values and the factorized matrices. The factorized matrices are generated based on weighted errors between the parameter values for the parameters of the linear layer and the approximated parameter values.

Abstract: A method includes receiving one or more training corpora for training a machine learning model having a plurality of encoder blocks, where each encoder block includes an attention layer and a feedforward network. The method also includes using the one or more training corpora to train an attention dictionary shared across the plurality of encoder blocks. Training the attention dictionary may include training attention parameters of the attention layer in each of the plurality of encoder blocks, and the attention parameters for a given encoder block among the plurality of encoder blocks may be a weighted combination of columns from the attention dictionary shared across the plurality of encoder blocks.

Abstract: A method of a server device is provided. The method of a server device includes retrieving a prediction input and a prediction setting, replacing at least one non-linear activation channel in a neural network with at least one replacement channel based on the received prediction setting, generating a prediction based on the received prediction input based on the neural network with the at least one replacement channel, and outputting the generated prediction.

Abstract: A melting pot includes a pot body and a float salvaging apparatus. The pot body is provided with a melting chamber having an open upper end. The float salvaging apparatus includes a hanging bracket, a bearing plate, a rotating plate and a drive assembly configured to drive pivoting of the rotating plate. The hanging bracket is disposed above the melting chamber. The rotating plate is pivotably disposed on the bearing plate, and the bearing plate and the rotating plate are provided on the hanging bracket between an initial position and a first salvaging position in a manner of moving up and down. The drive assembly is connected to the rotating plate.

Abstract: A melting pot includes a pot body and a float salvaging apparatus. The pot body is provided with a melting chamber having an open upper end. The float salvaging apparatus includes a hanging bracket, a bearing plate, a rotating plate and a drive assembly configured to drive pivoting of the rotating plate. The hanging bracket is disposed above the melting chamber. The rotating plate is pivotably disposed on the bearing plate, and the bearing plate and the rotating plate are provided on the hanging bracket between an initial position and a first salvaging position in a manner of moving up and down. The drive assembly is connected to the rotating plate.