Abstract: A land leveller includes a vehicle frame and a swing frame. The swing frame include a supporting beam assembly and connecting parts, the supporting beam assembly include multiple beam members, and the multiple beam members are connected sequentially at end parts, such that the multiple beam members form a closed shape. The connecting part is arranged at the connecting end part between two adjacent beam members. At least one connecting part is rotatably connected with the vehicle frame, and at least another connecting part is rotatably connected with a driving part.

Abstract: Methods, systems, and computer-readable storage media for receiving, by an operation guard system executed within a cloud platform, session information representative of a session of a user within the cloud platform, the session information including user information and operation information, determining, by the operation guard system, that the user is signed into a technical group for execution of an operation represented in the operation information, and in response, providing, by the operation guard system, a risk score associated with the operation, and determining, by the operation guard system and at least partially based on the risk score, that the operation is a risk-oriented operation based on the risk score, and in response, preventing execution of the operation and transmitting an alert.

Abstract: An electronic shelf label positioning system, an electronic shelf label and a guide rail. The electronic shelf label positioning system includes the electronic shelf label, the guide rail, a PDA and a background server. The electronic shelf label includes a main control SoC, a card reader IC, a screen and a power supply device. The main control SoC is configured to control the screen display and to communicate with an AP. The power supply device is configured to supply power to the electronic shelf label. The guide rail includes a guide rail identification area and a label area. The label area is installed with a plurality of wireless labels each having a unique non-repeated ID number. The guide rail identification area is installed with an identity recognition device, which includes a guide rail ID consisting of the ID numbers of the wireless labels sequentially arranged and summarized.

Abstract: A flexible display screen includes: a flexible substrate (1); an OLED device layer (2) formed on the flexible substrate (1); an encapsulation layer(3), disposed on the OLED device layer(2) and encapsulating the OLED device layer (2); and an encapsulation protection layer (4) formed on the encapsulation layer (3). The embodiments of the present disclosure also provide a flexible display device and a manufacturing method of the flexible display screen.

Abstract: Various examples are directed to systems and methods for operating a database management system (DBMS) in a cloud environment. An assembly worker executing at a first computing device may provide a first database protocol message generated by a process code executing at the first computing device. A translation engine executed at the cloud environment may translate the first database protocol message from a first format to a second format associated with a DBMS instance executing at the cloud environment to generate a translated first database protocol message. The translation engine may cause the translated first database protocol message to be provided to the DBMS instance and may receive, from the DBMS instance, a first reply corresponding to the translated first database protocol message. The first reply may be sent to the process code.

Abstract: Example embodiments of aspects of the present disclosure provide an example computer-implemented method for improved prompting of a machine-learned model. The example method can include obtaining an instructive sequence descriptive of an instructive query, an instructive response, and an instructive trace of intermediate states from the instructive query to the instructive response. The example method can include inputting, to a machine-learned model, the instructive sequence and an operative query, wherein the machine-learned model is configured to process the operative query with attention over the instructive sequence. The example method can include generating, using the machine-learned model and responsive to the operative query, an operative response.

Abstract: An example method for pretraining a machine-learned model is provided. The example method includes obtaining a plurality of different combinations of configuration parameters of a pretraining objective framework. The example method includes generating, using the pretraining objective framework, a plurality of corrupted training examples from one or more training examples, wherein the plurality of corrupted training examples are respectively generated according to the plurality of different combinations. The example method includes inputting the plurality of corrupted training examples into the machine-learned model, wherein the machine-learned model is configured to generate uncorrupted subportions corresponding to corrupted subportions of the corrupted training examples. The example method includes obtaining, from the machine-learned model, a plurality of outputs respectively generated by the machine-learned model based on the plurality of corrupted training examples.

Abstract: Systems and methods of the present disclosure are directed to a computer-implemented method. The method can include obtaining a machine-learned model comprising a plurality of model units, wherein each model unit comprises a plurality of parameters that are tied to a shared plurality of parameters. The method can include performing a first plurality of training iterations with the machine-learned model to adjust parameters of the shared plurality of parameters. The method can include detecting, based on the first plurality of training iterations, an occurrence of an untying condition. The method can include untying the parameters of one or more model units from the shared plurality of parameters. The method can include performing a second plurality of training iterations with the machine-learned model to adjust parameters of the one or more model units independent of the shared plurality of parameters.

Abstract: A system associated with quantifying a density level of tumor-infiltrating lymphocytes, based on prediction of reconstructed TIL information associated with tumoral tissue image data during pathology analysis of the tissue image data is disclosed. The system receives digitized diagnostic and stained whole-slide image data related to tissue of a particular type of tumoral data. Defined are regions of interest that represents a portion of, or a full image of the whole-slide image data. The image data is encoded into segmented data portions based on convolutional autoencoding of objects associated with the collection of image data. The density of tumor-infiltrating lymphocytes is determined of bounded segmented data portions for respective classification of the regions of interest. A classification label is assigned to the regions of interest. It is determined whether an assigned classification label is above a pre-determined threshold probability value of lymphocyte infiltrated.

Abstract: A system associated with quantifying a density level of tumor-infiltrating lymphocytes, based on prediction of reconstructed TIL information associated with tumoral tissue image data during pathology analysis of the tissue image data is disclosed. The system receives digitized diagnostic and stained whole-slide image data related to tissue of a particular type of tumoral data. Defined are regions of interest that represents a portion of, or a full image of the whole-slide image data. The image data is encoded into segmented data portions based on convolutional autoencoding of objects associated with the collection of image data. The density of tumor-infiltrating lymphocytes is determined of bounded segmented data portions for respective classification of the regions of interest. A classification label is assigned to the regions of interest. It is determined whether an assigned classification label is above a pre-determined threshold probability value of lymphocyte infiltrated.