Abstract: The present disclosure relates to manufacturing training data by leveraging an automated pipeline that manufactures visualization training datasets to train a machine learning model to convert a natural language utterance into meaning representation language logical form that includes one or more visualization actions. Aspects are directed towards accessing an original training dataset, a visualization query dataset, an incremental visualization dataset, a manipulation visualization dataset, or any combination thereof. One or more visualization training datasets are generated by: (i) modifying examples in the original training dataset, the visualization query dataset, or both to include visualization actions, (ii) generating examples, using the incremental visualization dataset, the manipulation visualization dataset, or both, that include visualization actions, or (iii) both (i) and (ii).

Abstract: An online uncapping system includes a sample intake rail, a scanning blocking mechanism, a sample container information scanning apparatus, an uncapping blocking mechanism, a sample tube clamping mechanism, a sample tube uncapping device, and a cap receiving mechanism. The scanning blocking mechanism is arranged on one side of the sample intake rail; the sample container information scanning apparatus is configured to read information about the sample tube blocked by the scanning blocking mechanism; the uncapping blocking mechanism is arranged on one side of the sample inlet rail and configured to block and release the sample tube; the sample tube clamping mechanism is configured to clamp the sample tube blocked by the uncapping blocking mechanism; the sample tube uncapping device is configured to uncover the sample tube blocked by the uncapping blocking mechanism; and the cap receiving mechanism is configured to collect the tube cap.

Abstract: An image generating method and apparatus, and a device and a medium are disclosed. The method comprises: acquiring weakly correlated image-text data pairs, and creating an image-text data set according to the weakly correlated image-text data pairs, wherein the weakly correlated image-text data pairs are image-text data pairs in which images and texts have weak correlations (S11); training, by using the image-text data set, an image generation model which is preconstructed on the basis of an adversarial network, so as to obtain a trained image generation model, wherein the image generation model includes a generator for generating an image, and a discriminator for identifying the authenticity of the image and calculating a corresponding loss value (S12); and after when text data to be processed has been acquired, generating, by using the trained image generation model, an image corresponding to the said text data (S13).

Abstract: Techniques are disclosed herein for transforming natural language conversations into a visual output. In one aspect, a computer-implement method includes generating an input string by concatenating a natural language utterance with a schema representation comprising a set of entities for visualization actions, generating, by a first encoder of a machine learning model, one or more embeddings of the input string, encoding, by a second encoder of the machine learning model, relations between elements in the schema representation and words in the natural language utterance based on the one or more embeddings, generating, by a grammar-based decoder of the machine learning model and based on the encoded relations and the one or more embeddings, an intermediate logical form that represents at least the query, the one or more visualization actions, or the combination thereof, and generating, based on the intermediate logical form, a command for a computing system.

Abstract: Provided are a display panel, a preparation method thereof, and a display device. The display panel includes a display substrate and a touch panel including multiple touch electrodes, At least one of which includes multiple grid patterns enclosed by metal wires. At least one grid pattern includes a first edge, second edge, third edge and fourth edge that form a ring. The first edge and the third edge extend in a second direction. The second edge and the fourth edge extend in a first direction. A shape of the grid pattern includes a first curved ring, a second curved ring, a third curved ring, or a fourth curved ring. A first edge and third edge of the first curved ring are curves curved towards a direction opposite to the first direction. A first edge and third edge of the second curved ring are curves curved towards the first direction.

Abstract: An online centrifugal system includes a conveying device, a balancing and weighing mechanism, a manipulator, and a centrifuge. The conveying device, the balancing and weighing mechanism and the centrifuge are sequentially arranged. The conveying device is used to convey a sample holder for holding blood collection tubes, the balancing and weighing mechanism is used to weigh an adapter, and the centrifuge is used to perform centrifugation to blood collection tubes. The manipulator is used to grab the blood collection tubes and the adapter respectively, to convey the blood collection tubes between the adapter and the conveying device, and convey the adapter between the centrifuge and the balancing and weighing mechanism. An inlet and outlet window is provided on the centrifuge for passage of the adapter. The balancing and weighing mechanism is used to balance the adapter, thereby meeting balancing needs of the adapter.

Abstract: This application discloses a method of determining a network path and a network device. The method includes: determining a first reachable network path that uses a source node as a start point and a directly connected node of the source node as an end point; selecting a first node from a to-be-added node set to obtain a second reachable network path that uses the source node as the start point and the first node as a relay; and when there are m paths that use a same destination node as end points in the first and second reachable network paths, comparing the m paths based on n target factors to obtain a first dominant network path, and updating the first reachable network path based on the first dominant network path, where m?2, and n?2.

Abstract: Techniques for using logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system. The chatbot system can input the utterance into a machine-learning model including a set of binary classifiers. Each binary classifier of the set of binary classifiers can be associated with a modified logit function. The method can also include the machine-learning model using the modified logit function to generate a set of distance-based logit values for the utterance. The method can also include the machine-learning model applying an enhanced activation function to the set of distance-based logit values to generate a predicted output. The method can also include the chatbot system classifying, based on the predicted output, the utterance as being associated with the particular class.

Abstract: In some aspects, a computing device may receive, at a data processing system, a set of utterances for training or inferencing with a named entity recognizer to assign a label to each token piece from the set of utterances. The computing device may determine a length of each utterance in the set and when the length of the utterance exceeds a pre-determined threshold of token pieces: dividing the utterance into a plurality of overlapping chunks of token pieces; assigning a label together with a confidence score for each token piece in a chunk; determining a final label and an associated confidence score for each chunk of token pieces by merging two confidence scores; determining a final annotated label for the utterance based at least on the merging the two confidence scores; and storing the final annotated label in a memory.

Abstract: The present application relates to the technical field of display, in particular to an organic electroluminescent material composition and an application thereof. The organic electroluminescent material composition of the present application comprise compound N and compound M, compound N and compound M cooperate with each other, resulting in an organic electroluminescent device comprising this material with better lifespan, lower driving voltage and higher efficiency at the same time.

Abstract: The present disclosure discloses an organic electroluminescent compound, a composition, a light emitting device and an application. The organic electroluminescent compound is a compound M having a structure shown in Formula (2), and Formula (2) is: in Formula (2), X1-X14 are selected from N or CR8, and R8 is selected from hydrogen or deuterium; L is selected from a connecting bond, substituted or unsubstituted C6-C30 arylenyl, and substituted or unsubstituted C3-C30 heteroarylenyl; Ar8-Ar9 are each independently selected from substituted or unsubstituted C6-C30 aryl and substituted or unsubstituted C3-C30 heteroaryl; and substituents are selected from one of or a combination of two of deuterium, halogen, cyano, C1-C6 alkyl, C3-C30 cycloalkyl, C6-C30 aryl and C3-C30 heteroaryl.

Abstract: A process system of a follower fixture for tank moving support is provided and includes a conveying table slidably provided with a bearing platform through a screw slide rail assembly and provided with an outer frame assembly; a lifting platform is slidably installed in the outer frame assembly; positions of the lifting platform and the bearing platform are corresponded up and down; the outer frame assembly is slidably provided with a robot component; both sides of the conveying table are provided with loading platforms; workpieces are placed on one loading platform, and a fixture system is placed on the other loading platform; the fixture system and the workpieces are both placed in the moving range of the robot component, and positions of the fixture system and the workpieces are corresponded. problems including processing flexibility cannot be realized can be solved.

Abstract: A data sending method includes a first computer device predicting a link quality of N links based on a sending queue length of each of the N links and historical statistical information, and selecting, based on the predicted link quality, a first target link for sending first data, to send the first data to a second computer device, where the first data is a part of data in a target data stream.

Abstract: The technology of this application relates to a communication method and apparatus. The communication method includes a data sending device obtaining to-be-sent data of a locally installed first application, where a destination address of the to-be-sent data is an address of a data receiving device. The data sending device determines an address or addresses of one or more network adapters of the data receiving device based on the address of the data receiving device, the data sending device encapsulates the to-be-sent data based on the network adapter address to obtain encapsulated data, and the data sending device sends the encapsulated data to the data receiving device through a plurality of links. A destination address of each link is one of network adapter addresses of the data receiving device, and a source address of each link is one of network adapter addresses of the data sending device.

Abstract: A probiotic-encapsulating gum Arabic (GA) composite fiber/capsule, including: lactic acid bacteria and an electrospun fiber or electrosprayed capsule, where the lactic acid bacteria are encapsulated in the electrospun fiber or electrosprayed capsule by electro-hydro dynamics (EHD) technology. The electrospun fiber or electrosprayed capsule is prepared by compounding a polymer matrix with GA, and the polymer matrix is polyvinyl alcohol (PVOH), polyvinylpyrrolidone (PVP), whey protein concentrate (WPC) or maltodextrin (MD). A preparation method and an application of the composite fiber/capsule are also provided.

Abstract: A method and apparatus for allocating a computing task of a neural network in heterogeneous resources, a computer device, and a storage medium. The method includes: acquiring task information of the computing task of the neural network and resource information of the heterogeneous resources; determining, according to the task information and the resource information, an allocation mode for allocating each subtask to the heterogeneous resources for execution and a task processing cost corresponding to each allocation mode; constructing a directed acyclic graph according to each allocation mode and each task processing cost; obtaining a value of a loss function corresponding to each allocation path according to the task processing cost corresponding to each subtask in an allocation path of the directed acyclic graph; and selecting a target allocation path according to the value of each loss function.

Abstract: Systems and methods are provided for data shuffling for distributed machine learning training, including each training node in the network receiving a shard of training data, wherein the training data set is divided into shards having data items. Each data item is assigned to a working set such that each of the working set includes data items from multiple shards. The training nodes perform training using the data items of a first working set that are in each node's shard. Upon completion of the training using the data items of the first working set, the training nodes performing training using the data items of a second working set that are in their shards; and while the training nodes are performing training on their respective subsets of shards of the second working set, the nodes randomly shuffling data items in the first working set to create a shuffled first working set.

Abstract: Systems and methods are configured to provide lifetime data valuations for a dataset that evolves across multiple machine learning training tasks by providing and updating path-dependent data valuations for data points in the dataset during each training task. A current machine learning training task may include splitting the dataset into multiple random mini-epochs and training the current machine learning model using a first random mini-epoch and an accuracy mini-epoch, which consists of high value data points from the path-dependent data valuations. The random and accuracy mini-epochs can be, during the training, iterated for a number of times during the training, while a second random mini-epoch is prefetch. During the training, the path-dependent data valuations can be updated based on data valuations during the current training and a similarity between the current machine learning model and prior trained machine learning models.

Abstract: Provided herein are methods for inhibiting ALPK1 kinase activity using a compound of Formula I, and compositions and methods for therapy, for example in treating Kawasaki Disease.

Abstract: Provided herein are compounds of Formula (I) or (II) and related compositions and methods for their use as inhibitors of alpha-kinase 1 (ALPK1) and in treating Kawasaki Disease.