Abstract: The present disclosure provides a polyethylene glycol-modified urate oxidase. At least 11 of the following amino acid sites in the urate oxidase have a PEG modification: T1, K3, K4, K30, K35, K76, K79, K97, K112, K116, K120, K152, K179, K222, K231, K266, K272, K285, K291, and K293.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining voice characteristics are provided. One of the methods includes: obtaining speech data of a speaker; inputting the speech data into a model trained at least by jointly minimizing a first loss function and a second loss function, wherein the first loss function comprises a non-sampling-based loss function and the second loss function comprises a Gaussian mixture loss function with non-unit multi-variant covariance matrix; and obtaining from the trained model one or more voice characteristics of the speaker.

Abstract: A permanent magnet direct-drive bogie and a rail vehicle thereof are disclosed.

Abstract: The disclosure belongs to the technical field of organic photoelectric materials, and discloses carbonyl containing organic electroluminescent materials and use thereof in OLED. The carbonyl containing organic electroluminescent materials have a structure of formula I, wherein R1 and R2 are different in Formula I, R1 and R2 are different electron-donating groups of aromatic ring derivatives; R1 is an electron-donating groups of hole transport host materials or electron-donating groups of bipolar transport host materials. The luminescent materials of the present disclosure are organic electroluminescent materials based on derivatives of a host material and a benzoyl group. They have both AIE and delayed fluorescence characteristics with high-efficiency solid-state emission, high exciton utilization, and bipolar features.

Abstract: The present disclosure relates to a system, method and non-transitory computer readable medium. The system includes at least one computer-readable storage medium including a set of instructions and at least one processor in communication with the at least one computer-readable storage medium. When executing the set of instructions, the at least one processor is directed to: obtain a plurality of sub-tables, each being associated with one or more fields of a target wide table; generate a plurality of intermediate tables, each being directly or indirectly based on one or more of the plurality of sub-tables, wherein at least two of the plurality of intermediate tables are generated in parallel; and determine the target wide table based on the plurality of intermediate tables.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining voice characteristics are provided. One of the methods includes: obtaining speech data of a speaker; inputting the speech data into a model trained at least by jointly minimizing a first loss function and a second loss function, wherein the first loss function comprises a non-sampling-based loss function and the second loss function comprises a Gaussian mixture loss function with non-unit multi-variant covariance matrix; and obtaining from the trained model one or more voice characteristics of the speaker.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for personalized speaker verification are provided. One of the methods includes: obtaining first speech data of a speaker as a positive sample and second speech data of an entity different from the speaker as a negative sample; feeding the positive sample and the negative sample to a first model for determining voice characteristics to correspondingly output a positive voice characteristic and a negative voice characteristic of the speaker; obtaining a gradient based at least on the positive voice characteristic and the negative voice characteristic; and feeding the gradient to the first model to update one or more parameters of the first model to obtain a second model for personalized speaker verification.

Abstract: Embodiments are generally directed to an adaptive deformable kernel prediction network for image de-noising. An embodiment of a method for de-noising an image by a convolutional neural network implemented on a compute engine, the image including a plurality of pixels, the method comprising: for each of the plurality of pixels of the image, generating a convolutional kernel having a plurality of kernel values for the pixel; generating a plurality of offsets for the pixel respectively corresponding to the plurality of kernel values, each of the plurality of offsets to indicate a deviation from a pixel position of the pixel; determining a plurality of deviated pixel positions based on the pixel position of the pixel and the plurality of offsets; and filtering the pixel with the convolutional kernel and pixel values of the plurality of deviated pixel positions to obtain a de-noised pixel.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining voice characteristics are provided. One of the methods includes: obtaining speech data of a speaker; inputting the speech data into a model trained at least by jointly minimizing a first loss function and a second loss function, wherein the first loss function comprises a non-sampling-based loss function and the second function comprises a Gaussian mixture loss function with non-unit multi-variant covariance matrix; and obtaining from the trained model one or more voice characteristics of the speaker.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for personalized speaker verification are provided. One of the methods includes: obtaining first speech data of a speaker as a positive sample and second speech data of an entity different from the speaker as a negative sample; feeding the positive sample and the negative sample to a first model for determining voice characteristics to correspondingly output a positive voice characteristic and a negative voice characteristic of the speaker; obtaining a gradient based at least on the positive voice characteristic and the negative voice characteristic; and feeding the gradient to the first model to update one or more parameters of the first model to obtain a second model for personalized speaker verification.

Abstract: The present subject matter relates to ATE luminogens for visualization and treatment of cancer, particularly AIE luminogenic probes for cancer cell visualization and discrimination, lysosome-targeting AIEgens for imaging and autophagy visualization, highly fluorescent AIE-active theranostic agents for monitoring drug distribution and having anti-tumor activity to specific cancer cells, probes comprising AIE luminogens for cancer cell imaging and staining, AIE luminogens having clusteroluminogenic features and applications thereof, and methods of preparing thereof.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining voice characteristics are provided. One of the methods includes: obtaining speech data of a speaker; inputting the speech data into a model trained at least by jointly minimizing a first loss function and a second loss function, wherein the first loss function comprises a non-sampling-based loss function and the second function comprises a Gaussian mixture loss function with non-unit multi-variant covariance matrix; and obtaining from the trained model one or more voice characteristics of the speaker.

Abstract: The present disclosure relates to a system, method and non-transitory computer readable medium. The system includes at least one computer-readable storage medium including a set of instructions and at least one processor in communication with the at least one computer-readable storage medium. When executing the set of instructions, the at least one processor is directed to: obtain a plurality of sub-tables, each being associated with one or more fields of a target wide table; generate a plurality of intermediate tables, each being directly or indirectly based on one or more of the plurality of sub-tables, wherein at least two of the plurality of intermediate tables are generated in parallel; and determine the target wide table based on the plurality of intermediate tables.

Abstract: Implementations of the present specification provide a voiceprint recognition method and device. The method includes: extracting a first spectral feature from speaker audio; inputting the speaker audio to a memory deep neural network (DNN), and extracting a bottleneck feature from a bottleneck layer of the memory DNN, where the memory DNN includes at least one temporal recurrent layer and the bottleneck layer, an output of the at least one temporal recurrent layer is connected to the bottleneck layer; forming an acoustic feature of the speaker audio based on the first spectral feature and the bottleneck feature; extracting an identity authentication vector corresponding to the speaker audio based on the acoustic feature; and performing speaker recognition by using a classification model and based on an identity authentication vector (i-vector).

Abstract: A speech wakeup method, apparatus, and electronic device are disclosed in embodiments of this specification. The method includes: inputting speech data to a speech wakeup model trained with general speech data; and outputting, by the speech wakeup model, a result for determining whether to execute speech wakeup, wherein the speech wakeup model includes a Deep Neural Network (DNN) and a Connectionist Temporal Classifier (CTC).

Abstract: A system for programming a protection device for a molding machine includes a controller for actuating a plurality of molding machine actuators in an actuation sequence, each distinct actuation constituting a respective machine component actuation of an associated machine component. An HMI is operable to: present a GUI specific to a chosen machine component actuation; and for each of a plurality of other machine component actuations, define within the GUI, based on operator input, a rule specifying a state of the chosen machine component actuation relative to a state of the other machine component actuation for preventing interference between the two machine component actuations. The controller is configured, based on the rules defined within the GUI, to trigger an action, upon violation of any one of the rules, for reducing a risk of interference between the chosen machine component actuation and a respective one of the other machine component actuations.