Abstract: Embodiments of the present disclosure provide a chuck structure of a semiconductor cleaning apparatus and a semiconductor cleaning apparatus. The chuck structure includes a chuck base, a gas inlet control device, and a first gas channel and a second gas channel arranged in the chuck base and both configured to spray a gas toward the to-be-processed workpiece. A gas spray direction of the first gas channel is inclined toward the outside of the surface of the to-be-processed workpiece that is opposite to the chuck base. A gas spray direction of the second gas channel is perpendicular to the surface of the to-be-processed workpiece that is opposite to the chuck base. The gas inlet control device is configured to selectively supply the gas to at least one of the first gas channel and the second gas channel to cause the to-be-processed workpiece to be able to float at different height positions.

Abstract: Methods, systems, and apparatus for providing driving insights includes a driving insights module programed to receive a set of vehicle conditions, receive a fuel consumption database, and calculate a fuel savings of the vehicle based upon the set of vehicle conditions and the fuel consumption database. For example, the driving insights module can calculate the fuel saved by using an alternative power source compared to the fuel that would have been used by a vehicle operated by an internal combustion engine. In response to detecting an ignition off event, the driving insights module can generate the fuel savings estimate. The driving insights module can further output for display the fuel savings estimate.

Abstract: The present disclosure provides an ultra-high-pressure hose joint assembly, including joint components and a tube body. The joint component includes: a mandrel, the tube body being sleeved over the outer side of the mandrel, the mandrel being in communication with the tube body; a connector, at least a part of the connector being sleeved on the mandrel; and a positioning element, the positioning element being located at the connection between the mandrel and the connector, and the positioning element being used to restrict the relative position between the connector and the mandrel. The ultra-high-pressure hose joint assembly in the present disclosure solves the technical problem in the related art that ultra-high-pressure hose joint assemblies are prone to joint leakage.

Abstract: A processor-implemented method includes processing a plurality of first information related to autonomous driving based on an artificial intelligence (AI) model comprising a first AI network and a plurality of second AI networks associated with one or more driving skills, and determining execution task information for autonomous driving, based on an output corresponding to the plurality of first information, generated by one or more second AI networks of the plurality of second AI networks, wherein the processing includes determining the one or more second AI networks associated with each of the plurality of first information based on the first AI network, and processing the plurality of first information based on the one or more second AI networks.

Abstract: Systems, methods, and other embodiments described herein relate to improving the safety of vehicles by determining lane-level risks of road segments. In one embodiment, a method includes acquiring data about a segment of a roadway. The data indicates at least information about traffic on the segment. The method includes determining a lane-level risk associated with a safety event occurring on the segment using a likelihood model and an impact model. The method includes providing the lane-level risk to cause an approaching vehicle to selectively adapt operation.

Abstract: The present disclosure describes techniques for processing an image using a machine learning model. An instruction of decomposing the image into a plurality of layers is received. Visual features of the image are generated. Embeddings indicative of the plurality of layers are generated by a first sub-model of the machine learning model based on the visual features and textual tokens representative of the instruction. Layer images corresponding to the plurality of layers are generated by a second sub-model of the machine learning model based on the embeddings.

Abstract: Techniques for implementing automatic layer decomposition are provided. An image comprising a plurality of objects is received. Object detection results are generated based on detecting the plurality of objects in the image. The object detection results comprising an object detection result corresponding to each of the plurality of objects. Textual descriptions of the image are generated. The textual descriptions comprise a textual description corresponding to each of the plurality of objects. The object detection result is associated with the textual description corresponding to each of the plurality of objects. Depth estimation results are generated by predicting a depth map of the image. The depth estimation results comprise a depth estimation result corresponding to each of the plurality of objects. The plurality of objects are merged into layers based on the object detection result, the textual description, and the depth estimation result corresponding to each of the plurality of objects.

Abstract: The present disclosure describes techniques for generating video descriptions using a machine learning model. A plurality of sets of visual tokens corresponding to a plurality of frames of a video is generated. A first type of tokens is generated by implementing temporal pooling on the plurality of sets of visual tokens corresponding to the plurality of frames. A second type of tokens is generated by compressing each of the plurality of sets of visual tokens corresponding to each of the plurality of frames. A third type of tokens is generated by applying cross-attention between each of the plurality of sets of visual tokens and a fourth type of tokens including text tokens generated based on an input text query. A text description of the video is generated based on the first type of tokens, the second type of tokens, the third type of tokens, and the fourth type of tokens.

Abstract: An elevator safety system and a method for testing functionality thereof. A method for testing functionality of an elevator braking system. In the method, a first connector is connected in parallel to both ends of a first contact, then a second contact is transitioned to a disconnected state, to determine whether the functionality of a second holding brake is operating normally based on the motion state of a car after the second contact is transitioned to a disconnected state. A second connector is further connected in parallel to both ends of the second contact, then the first contact is transitioned to a disconnected state, to determine whether the functionality of a first holding brake is operating normally based on the motion state of the car after the first contact is transitioned to a disconnected state.

Abstract: Methods, systems, and apparatus for providing driving insights includes a driving insights module programed to receive a set of vehicle conditions, receive a fuel consumption database, and calculate a fuel savings of the vehicle based upon the set of vehicle conditions and the fuel consumption database. For example, the driving insights module can calculate the fuel saved by using an alternative power source compared to the fuel that would have been used by a vehicle operated by an internal combustion engine. In response to detecting an ignition off event, the driving insights module can generate the fuel savings estimate. The driving insights module can further output for display the fuel savings estimate.

Abstract: An identity management system may be associated with a software plug-in for input detection of a website. In some examples, the plug-in may obtain, via an image capturing system, an image of the website that includes a set of inputs, where the set of inputs includes an interactive interface element. Using the obtained image, a set of location predictions for the set of inputs of the website may be generated via a machine learning (ML) model. Further, the plug-in may obtain a set of locations of the set of inputs based on generating the set of location predictions. Thus, the plug-in may automatically, and in response to obtaining the set of locations of the set of inputs of the website, input content into the set of inputs of the website, select an interactive interface element on the website, or both, on the behalf of the user.

Abstract: The present disclosure describes techniques for searching editing components based on text using a machine learning model. A plurality of visual embeddings indicative of a plurality of visual editing components is acquired by the machine learning model. The plurality of visual embeddings indicative of the plurality of visual editing components is projected into a common space by a first sub-model of the machine learning model. A text query input is received by a user. A text embedding indicative of the text query is generated. The text embedding is projected into the common space by a second sub-model of the machine learning model. At least one visual editing component among the plurality of visual editing components is determined based on the projected text embedding and the plurality of projected visual embeddings in the common space. Information indicative of the at least one visual editing component is displayed via a user interface.

Abstract: A method may include obtaining a first image associated with an image to be segmented, and performing an iteration process for obtaining a target image. The iteration process may include one or more iterations each of which includes: obtaining an image to be modified; obtaining one or more modifications performed on the image to be modified; generating a second image by inputting the image to be segmented, the image to be modified, and the one or more modifications into the image segmentation model; in response to determining that the second image satisfies the first condition, terminating the iteration process by determining the second image as the target image; or in response to determining that the second image does not satisfy the first condition, initiating a new iteration of the iteration process by determining the second image as the image to be modified.

Abstract: The present disclosure describes techniques for generating image descriptions using a machine learning model. Mixture of Experts (MoE) blocks are incorporated into a plurality of sub-models of the machine learning model. The first sub-model of the machine learning model comprises at least one first MoE block including a first plurality of experts. A second sub-model of the machine learning model comprises at least one second MoE block including a second plurality of experts. Only a subset of the first plurality of experts is activated to generate visual tokens based on an input image. Only a subset of the second plurality of experts is activated to project the visual tokens into an input space of the third sub-model. A text description of the input image is output by the third sub-model of the machine learning model.

Abstract: A method for preparing bisphenol includes: mixing and uniformly stirring cardanol, formaldehyde and an alkaline catalyst, and reacting at a certain temperature to obtain a hydroxymethylation product A; washing the product A multiple times with water until the pH value is neutral, and then centrifuging to remove water to obtain a product B; mixing and uniformly stirring the product B with phenol and an acidic catalyst, and reacting at a certain temperature to obtain a product C after a phenolic alcohol reaction; washing the product C with water until neutral, and distilling same under reduced pressure to obtain a cardanol-based bisphenol product. Cardanol is used which has the structural characteristics of both a benzene ring and an alkane chain, and when protecting phenolic hydroxyl which has high reaction activity, a bisphenol structure which has both benzene ring rigidity and cardanol upper alkane long-chain toughness is obtained.

Abstract: An electronic device includes one or more processors configured to collect an original image for a target scene, and generate a corrected modified image by reflecting a predicted lighting condition of the original image in response to inputting the original image to a first artificial intelligence (AI) model, wherein the first AI model is generated based on a second AI model trained based on any one or any combination of any two or more of a sample image collected under a plurality of lighting conditions, a first actual image comprising a lighting condition of the sample image, and a second actual image comprising color information of an object in the sample image.

Abstract: The present disclosure describes techniques for searching editing components based on text using a machine learning model. A plurality of visual embeddings indicative of a plurality of visual editing components is acquired by the machine learning model. The plurality of visual embeddings indicative of the plurality of visual editing components is projected into a common space by a first sub-model of the machine learning model. A text query input is received by a user. A text embedding indicative of the text query is generated. The text embedding is projected into the common space by a second sub-model of the machine learning model. At least one visual editing component among the plurality of visual editing components is determined based on the projected text embedding and the plurality of projected visual embeddings in the common space. Information indicative of the at least one visual editing component is displayed via a user interface.

Abstract: An example operation includes one or more of determining that a first vehicle is unable to receive a charge at a charging station, determining that a second vehicle is unable to receive a charge at the charging station, and notifying one or more other vehicles of an inability to receive a charge at the charging station.

Abstract: A method may include obtaining a first image associated with an image to be segmented, and performing an iteration process for obtaining a target image. The iteration process may include one or more iterations each of which includes: obtaining an image to be modified; obtaining one or more modifications performed on the image to be modified; generating a second image by inputting the image to be segmented, the image to be modified, and the one or more modifications into the image segmentation model; in response to determining that the second image satisfies the first condition, terminating the iteration process by determining the second image as the target image; or in response to determining that the second image does not satisfy the first condition, initiating a new iteration of the iteration process by determining the second image as the image to be modified.

Abstract: A processor-implemented method with image generation includes obtaining a first image, determining predicted texture information of a target image corresponding to the first image through a texture prediction model, based on the first image, determining predicted color information of the target image through a color prediction model, based on the first image, and generating the target image based on the first image, using the predicted texture information and the predicted color information, wherein a format of the target image is different from that of the first image.