Abstract: The present disclosure provides a privacy computing method based on homomorphic encryption, which includes steps as follows. The ciphertext data is received, where the ciphertext data has a floating-point homomorphic encryption data structure, and the floating-point homomorphic encryption data structure of the ciphertext data includes the ciphertext mantissa, exponent parameter and gain parameter. The gain parameter sets the precision of the floating point corresponding to the ciphertext mantissa. The exponent parameter is adapted to multiplication or division. The artificial intelligence model performs operations on the ciphertext data to return the ciphertext result.

Abstract: A textile includes: (1) a matrix; and (2) particulate fillers dispersed within the matrix. The textile has a transmittance of infrared radiation at a wavelength of 9.5 ?m of at least about 40%, and the textile has a weighted average reflectivity of radiation over a wavelength range of 0.3 ?m to 2 ?m of at least about 40%.

Abstract: The federated learning system includes a moderator and client devices. Each client device performs a method for verifying model update as follows: receiving a hash function and a general model; training a client model according to the general model and raw data; calculating a difference as an update parameter between the general model and the client model, sending the update parameter to the moderator; inputting the update parameter to the hash function to generate a hash value; sending the hash value to other client devices, and receiving other hash values; summing all the hash values to generate a trust value; receiving an aggregation parameter calculated according to the update parameters; inputting the aggregation parameter to the hash function to generate a to-be-verified value; and updating the client model according to the aggregation parameter when the to-be-verified value equals the trust value.

Abstract: A setting method of in-memory computing simulator includes: performing a plurality of test combinations by an in-memory computing device and recording a plurality of first estimation indices corresponding to the plurality of test combinations respectively, wherein each of the plurality of test combinations includes one of a plurality of neural network models and one of a plurality of datasets, executing a simulator according to the plurality of test combinations by a processing device and recording a plurality of second estimation indices corresponding to the plurality of test combinations respectively, wherein the simulator has a plurality of adjustable settings; calculating a correlation sum according to the plurality of first estimation indices and the plurality of second estimation indices by the processing device, and performing an optimal algorithm to search an optimal parameter in the setting space constructed by the plurality of settings so that the correlation sum is maximal.

Abstract: A sample processing kit including a centrifugal microfluidic component and a collection component detachably fitted into the microfluidic component is provided. Upon the application of the sample processing kit, target molecules or cells may be separated and collected by the collection component for further experimentation.

Abstract: A sample processing kit including a centrifugal microfluidic component and a collection component detachably fitted into the microfluidic component is provided. Upon the application of the sample processing kit, target molecules or cells may be separated and collected by the collection component for further experimentation.