Abstract: The present disclosure relates to a method for fabricating a touch substrate, a touch substrate and a touch device. The method includes: forming, through a splicing exposure process, a first electrode layer including a metal strip in an edge region thereof and a first metal mesh pattern connected with the metal strip; forming, on one side of the first electrode layer and through a splicing exposure process, a second electrode layer including a metal strip in an edge region thereof and a second metal mesh pattern connected with the metal strip and insulated from the first metal mesh pattern, the metal strip of the first electrode layer directly contacting the metal strip of the second electrode layer to form a metal stack; and forming a wire electrically connected with one of the first and metal mesh patterns of the first and second electrode layers by using the metal stack.

Abstract: The present invention provides a method for preparing battery-grade anhydrous iron phosphate from liquid crude monoammonium phosphate, and belongs to the technical field of chemical industry production. In the present invention, ferrous sulfate solution and liquid crude monoammonium phosphate are used as raw materials, and ferrous iron is oxidized to ferric iron and separates out iron phosphate precipitate under the action of an oxidizing agent to obtain iron phosphate intermediate slurry; and then battery-grade anhydrous iron phosphate is finally obtained through solid-liquid separation, washing, aging, solid-liquid separation, washing, drying, dehydration and breaking up. The method provided by the present invention realizes the resource utilization of liquid crude monoammonium phosphate, has simple process and convenient operation and produces less waste water.

Abstract: A driving backplane, a manufacturing method thereof, a light-emitting substrate are provided. In the driving backplane, a first conductive layer on a substrate; a first organic film layer is between the first conductive layer and second conductive layer, the first organic film layer has first via holes; a first inorganic film layer is on one side of first organic film layer away from the substrate; the second conductive layer is on one side of first inorganic film layer away from first organic film layer, the second conductive layer and the first conductive layer are connected through the first via holes; orthographic projections of the first conductive layer and second conductive layer have an overlapping region, the overlapping region includes a region including orthographic projection of the first via holes, and a first region, an orthographic projection of the first inorganic film layer covers parts of the first region.

Abstract: Generally discussed herein are devices, systems, and methods for generating an embedding that is both local string dependent and global string dependent. The generated embedding can improve machine learning (ML) model performance. A method can include converting a string of words to a series of tokens, generating a local string-dependent embedding of each token of the series of tokens, generating a global string-dependent embedding of each token of the series of tokens, combining the local string dependent embedding the global string dependent embedding to generate an n-gram induced embedding of each token of the series of tokens, obtaining a masked language model (MLM) previously trained to generate a masked word prediction, and executing the MLM based on the n-gram induced embedding of each token to generate the masked word prediction.

Abstract: Provided by a bandpass filter circuit and a multiplexer. The bandpass filter circuit includes at least one electromagnetic LC filter circuit and at least one acoustic wave resonance unit. The at least one acoustic wave resonance unit includes an input port, an output port, at least one circuit element and at least three resonators. The at least one electromagnetic LC filter circuit is electrically connected to the at least one acoustic wave resonance unit, and the at least three resonators include at least one first resonator and at least one second resonator. In a case where the at least one first resonator includes one first resonator, the first resonator is connected in series between the input port and the output port.

Abstract: A human-computer interaction method and apparatus. Said method may include: receiving information of at least one modality of a user (201); identifying, on the basis of the information of the at least one modality, intention information of the user and user emotional features corresponding to the intention information (202); determining, on the basis of the intention information, reply information to the user (203); selecting, on the basis of the user emotional features, character emotional features to be fed back to the user (204); and generating, on the basis of the character emotional features and the reply information, a broadcast video of an animated character corresponding to the character emotional features (205).

Abstract: A connector for use with high-speed signals. The connector includes a connector subassembly having conductors disposed in groups and a shielding shell at least partially encircling each group. Each conductor includes a mating end and a mounting end opposite the mating end. The shielding shell includes openings that expose contact surfaces of the conductors at the ends. The shielding shell is formed by attaching a second shell part to a first shell part. The first shell part has plateaus and valleys, and the second shell part is attached to the first shell part at the valleys, such that tubular structures are formed. The shielding shell includes contact surfaces disposed on the same planes with the contact surfaces of the conductors at the ends, respectively. Such a configuration meets signal integrity requirements in connectors designed for 64 Gbps and beyond, while conforming to a standard that constrains mating and mounting interfaces.

Abstract: Provided are a speech recognition method, a speech recognition apparatus, a computer readable storage medium, and an electronic device. The method comprises: obtaining a sample speech signal, decoding the sample speech signal, obtaining a decoding result, and extracting a first feature from the decoding result; extracting a target speech segment from the sample speech signal, obtaining a log magnitude spectrum of the target speech segment, and determining a second feature according to the log magnitude spectrum; combining the first feature and the second feature to obtain a third feature; training an untrained classifier by using the third feature so as to obtain a trained classifier; and obtaining a third feature to be recognized of a speech signal to be recognized, so as to determine whether the third feature to be recognized comprises a prepositive word.

Abstract: System and method for aspect-level sentiment classification. The system includes a computing device, the computing device has a processer and a storage device storing computer executable code. The computer executable code is configured to: receive a sentence having a labeled aspect term and context; convert the sentence into a dependency tree graph; calculate an attention matrix of the dependency tree graph based on one-hop attention between any two nodes of the graph; calculate multi-head attention diffusion for any two nodes from the attention matrix; obtain updated embedding of the graph using the multi-head diffusion attention; classify the aspect term based on the updated embedding of the graph to obtain predicted classification of the aspect term; calculate loss function based on the predicted classification and the ground truth label of the aspect term; and adjust parameters of models in the computer executable code based on the loss function.

Abstract: A custom tone and vocal synthesis method and apparatus, an electronic device, and a storage medium. The synthesis method comprises: training a first neural network by means of a speaker record sample to obtain a speaker recognition model, the output training result of the first neural network being a speaker vector sample (S102); training a second neural network by means of an unaccompanied vocal singing sample and the speaker vector sample to obtain an unaccompanied singing synthesis model (S104); inputting a speaker record to be synthesized into the speaker recognition model to obtain speaker information output by the intermediate hidden layer of the speaker recognition model (S106); and inputting unaccompanied singing music information to be synthesized and the speaker information into the unaccompanied singing synthesis model to obtain a synthesized custom tone and vocal (S108).

Abstract: System and method for aspect-level sentiment classification. The system includes a computing device, the computing device has a processer and a storage device storing computer executable code. The computer executable code is configured to: receive an aspect term-sentence pair; embed the aspect term-sentence pair; parse the sentence using multiple parsers to obtain dependency trees, and perform edge union to obtain a merged graph; combine the embedding and the merged graph to obtain a relation graph; perform a relation graph neural network on the relation graph; extract hidden representation of the aspect term from updated relation neural network; and classify the aspect term based on the extracted representation to obtain a predicted classification label of the aspect term. During training, the computer executable code is further configured to calculate a loss function based on the predicted label and the ground truth label, and adjust parameters of models.

Abstract: Provided is a method and a device for protein-protein docking based on identification of a low-entropy hydration layer on a protein surface. Hydrophobic groups on a protein surface and hydrophobic groups containing a small amount of oxygen and nitrogen atoms, as well as some hydrophilic groups in intramolecular hydrogen bonds formed on the protein surface are identified as low-entropy areas. In a computer program, according to a low-entropy hydration layer theory on the protein surface, some nitrogen and oxygen hydrophilic atoms of the protein are changed into hydrophobic carbon atoms; the protein surface is cut into multiple planes, atoms in a hydrophobic connection region are selected in each plane, an area and a shape of surface atoms are calculated in each hydrophobic connection region, and a plane is selected with a maximum hydrophobic connection area; a protein-protein docking site is predicted using the connection region as a possible docking site.

Abstract: The present disclosure relates to a sequenced data processing method and device, and a text processing method and device, and relates to the field of data processing.

Abstract: A cosmetic composition for makeup removal is in the form of an oil-in-water emulsion. The composition includes a lipophilic phase having a content by weight between 35% and 60% with respect to the total weight of the composition. The composition has a non-ionic hydrophilic surfactant assembly with at least one alkylpolyglucoside. The composition further includes at least one gelling agent having a content by weight between 0.3% and 0.8% with respect to the total weight of the composition. The gelling agent is at least one clay and/or at least one polysaccharide gum.

Abstract: Disclosed among other aspects is a power supply such as may be used in a charged particle inspection system. The power supply includes a direct current source such as a programmable linear current source connected to a controlled voltage source where the control signal for the controlled voltage source is derived from a measured voltage drop across the direct current source.

Abstract: System and method for aspect-level sentiment classification. The system includes a computing device, the computing device has a processer and a storage device storing computer executable code. The computer executable code is configured to: receive a sentence having a labeled aspect term and context; convert the sentence into a dependency tree graph; calculate an attention matrix of the dependency tree graph based on one-hop attention between any two nodes of the graph; calculate multi-head attention diffusion for any two nodes from the attention matrix; obtain updated embedding of the graph using the multi-head diffusion attention; classify the aspect term based on the updated embedding of the graph to obtain predicted classification of the aspect term; calculate loss function based on the predicted classification and the ground truth label of the aspect term; and adjust parameters of models in the computer executable code based on the loss function.

Abstract: A processing unit can operate a first recurrent computational model (RCM) to provide first state information and a predicted result value. The processing unit can operating a first network computational model (NCM) to provide respective expectation values of a plurality of actions based at least in part on the first state information. The processing unit can provide an indication of at least one of the plurality of actions, and receive a reference result value, e.g., via a communications interface. The processing unit can train the first RCM based at least in part on the predicted result value and the reference result value to provide a second RCM, and can train the first NCM based at least in part on the first state information and the at least one of the plurality of actions to provide a second NCM.

Abstract: In some examples, a computing device refines feature information of query text. The device repeatedly determines attention information based at least in part on feature information of the image and the feature information of the query text, and modifies the feature information of the query text based at least in part on the attention information. The device selects at least one of a predetermined plurality of outputs based at least in part on the refined feature information of the query text. In some examples, the device operates a convolutional computational model to determine feature information of the image. The device network computational models (NCMs) to determine feature information of the query and to determine attention information based at least in part on the feature information of the image and the feature information of the query. Examples include a microphone to detect audio corresponding to the query text.