Abstract: Provided are a method, system and device for global path planning for an unmanned vehicle in an off-road environment. The method includes: obtaining satellite elevation data and a satellite remote sensing image of a current off-road environment; constructing a digital elevation model (DEM); determining slope and land surface relief of each grid in the current off-road environment; performing gray processing on the satellite remote sensing image to obtain grayscale values of the grids; determining traversal costs of the grids corresponding to different ground types; constructing a global grid map based on the slope and the land surface relief of each grid, as well as the traversal costs corresponding to the different ground types; determining a rugged terrain potential field and path costs; and searching for paths using a Bresenham's line algorithm and Theta* algorithm based on the rugged terrain potential field and the path costs, to generate a global path.

Abstract: The present application discloses a preparation method of SM non-woven fabrics for roof anti-slip, which belongs to the technical field of roofing materials, comprising preparing spunbond non-woven fabric raw materials, preparing spunbond non-woven fabrics, preparing meltblown non-woven fabric raw materials, preparing primary SM non-woven fabrics, and post-processing; the spunbond non-woven fabric raw materials are prepared by uniformly mixing polypropylene with a low melt flow index, polypropylene with a high melt flow index, sodium alginate, antioxidant 1010, zinc stearate, ultraviolet absorber UV-531, polyvinyl alcohol, reinforcing agent, adhesive agent, and nano titanium dioxide. The present application can avoid the problem that the SM non-woven fabrics cannot be fully bonded together and are easy to delaminate when being combined, can also solve the problem of fabric breakage during high-speed production, and can also improve the wear resistance, strength, and stiffness of SM non-woven fabrics.

Abstract: A technique is described for compressing input information fed to a machine-trained generative model. The technique includes: receiving original input information having a plurality of sentences; performing word-level encoding of the original input information using a first part of a machine-trained transformer model, to provide word-level encoded information; performing sentence-level encoding of the word-level encoded information using a second part of the machine-trained transformer model, to provide scores associated with the first plurality of sentences; selecting a subset of the sentences in the original input information based on the scores, to provide modified input information; and providing the modified input information to the machine-trained generative model. The operation of word-level encoding performs parallel processing on portions of the original input information.

Abstract: The disclosure herein describes training a text processing model to generate model output text data using input text data and a sentence count. A training data entry including input text data and output text data is obtained. A sentence count of the output text data is determined, and the output text data is labeled with a sentence count label and a sentence number label. Model output text data is generated with a text processing model using the input text data and determined sentence count as input data. Loss data associated with a difference between the generated model output text data and the labeled output text data is determined and the text processing model is adjusted using the determined loss data. The use of labeled output text data enables the model to be trained to produce output text data with a target sentence count in a computationally efficient manner.

Abstract: Example solutions for image paragraph captioning use a first vision language model to generate visual information (comprising text) for an image. The visual information may include tags, an initial image caption, and information on objects within the image (e.g., further tags and captions, and object attributes and locations within the image). In some examples, the visual information further includes visual clues. A generative language model generates a plurality of image story caption candidates (e.g., descriptive paragraphs) from the visual information. A second vision language model evaluates the plurality of image story caption candidates and selects a caption as the final output caption.