Abstract: This application provides a fastening apparatus, a vehicle door, a vehicle, and a device, and relates to the mechanical field. The apparatus includes: a multi-stage telescopic member, where the multi-stage telescopic member includes a plurality of components that are slidably sleeved sequentially, a component located at the top is a top component, and the top component is configured to connect to a mobile apparatus, so that the multi-stage telescopic member is extended or retracted as the mobile apparatus moves; and at least one fastening point, where the at least one fastening point is disposed on the multi-stage telescopic member, the at least one fastening point is configured to fasten a harness, one end of the harness is configured to connect to the mobile apparatus, and the other end of the harness is configured to connect to a power supply apparatus.

Abstract: A computing system is disclosed that includes a processor and memory. The memory stores instructions that, when executed by the processor, cause the processor to perform several acts. The acts comprise receiving conversational data indicative of an interaction between a client computing device and a generative model. The conversational data is provided as input into an intent classification module and the intent classification module produces an output indicative of a user intent based upon the conversational data. An anchor generation module generates anchor text indicative of portions of the conversational data correlated with the user intent. A content query based upon the anchor text is generated and content responsive to the content query is obtained and presented at the client computing device.

Abstract: A transformer-based neural network includes at least one mask attention network (MAN). The MAN computes an original attention data structure that expresses influence between pairs of data items in a sequence of data items. The MAN then modifies the original data structure by mask values in a mask data structure, to produce a modified attention data structure. Compared to the original attention data structure, the modified attention data structure better accounts for the influence of neighboring data items in the sequence of data items, given a particular data item under consideration. The mask data structure used by the MAN can have static and/or machine-trained mask values. In one implementation, the transformer-based neural network includes at least one MAN in combination with at least one other attention network that does not use a mask data structure, and at least one feed-forward neural network.

Abstract: An information retrieval technique uses one or more machine-trained models to generate one or more metadata embeddings. The technique then combines a query embedding with the metadata embedding(s). In some cases, the technique performs this operation using a graph convolution operation. This yields an augmented embedding. The technique then uses the augmented embedding to retrieve at least one item. The augmented embedding lies in the same vector space as target-item embeddings associated with candidate target items. Otherwise, the vector spaces associated with the query embedding and metadata embedding(s) can be different. In some implementations, the technique use dense retrieval, which enables the technique to deliver output results in real time.

Abstract: A method of interfacing a discrete digital workshop information system is provided, where the information system includes mutual integration among product lifecycle management (PLM), enterprise resource planning (ERP), a manufacturing execution system (MES), an energy management system (EMS), and a warehouse management system (WMS) of a finished product. A method of interfacing based on a data dictionary fusing different function datasets is proposed to solve the problems of a current discrete industry information system, such as single in function, a small amount of integrated information, a large number of “information islands” existing, incapable of achieving full-process informatization management and control, difficulty in product quality tracing and the like. By the method, it is possible to realize flexible production in a discrete manufacturing industry, precise management and control of a production process, significant improvement in product quality and significant reduction in operating costs.

Abstract: A cooling panel for a melter is disclosed that includes first and second outer walls and a plurality of side walls coupled to the first and second outer walls. The first and second outer walls and the plurality of side walls define an interior space. Each of the first and second outer walls, moreover, has a plurality of openings. The cooling panel further includes a plurality of baffles disposed in the interior space. The baffles include a plurality of projections. The respective openings of the outer walls and projections of the baffles fit together and are connected from outside of the cooling panel so that the outer walls and the baffles are fixed together, and the side walls are fixed to the outer walls so that the cooling panel is fluid-tight.

Abstract: A technique serves information items associated with at least two item-serving modes using shared resources. For example, the technique delivers dynamic search ads in a dynamic search ad (DSA) mode using processing resources that were originally developed for serving text ads in a text ad (TA) mode. In a first aspect, the technique repurposes a part of the TA-mode processing flow to find a set of landing pages associated with a query. In a second aspect, the technique performs further processing based on output results generated by the TA-mode processing flow, with the objective of identifying a set of landing pages associated with the query. Overall, the technique reduces the consumption of resources in the serving of ads, and simplifies the maintenance of an ad-serving system. The technique also allows one mode to use algorithmic advances developed for use in another mode.

Abstract: Methods, systems, and devices supporting configurable resistivities for lines in a memory device, such as access lines in a memory array are described. For example, metal lines at different levels of a memory device may be oxidized to different extents in order for the lines at different levels of the memory device to have different resistivities. This may allow the resistivity of lines to be tuned on a level-by-level basis without altering the fabrication techniques and related parameters used to initially form the lines at the different levels, which may have benefits related to at least reduced cost and complexity. Lines may be oxidized to a controlled extent using either a dry or wet process.

Abstract: Technologies described herein relate to dense retrieval and ranking of search results. A query indicating a computing context or user input is received. An embedding of the query is computed by way of a first encoder, and candidate results selected from a pool of potential results based upon the embedding of the query and embeddings of the potential results. A similarity score for a first of the candidate results is computed by way of a second encoder trained based upon an order metric that defines a ranking over a training set of potential results. The first encoder is trained based upon output of the second encoder prior to computing the embedding of the query. The candidate results are ranked based upon the similarity score of the first candidate result, and results responsive to the query are identified based upon the ranking. The identified results are output to a computing device.

Abstract: Technologies described herein relate to dense retrieval and ranking of search results. A query indicating a computing context or user input is received. An embedding of the query is computed by way of a first encoder, and candidate results selected from a pool of potential results based upon the embedding of the query and embeddings of the potential results. A similarity score for a first of the candidate results is computed by way of a second encoder trained based upon an order metric that defines a ranking over a training set of potential results. The first encoder is trained based upon output of the second encoder prior to computing the embedding of the query. The candidate results are ranked based upon the similarity score of the first candidate result, and results responsive to the query are identified based upon the ranking. The identified results are output to a computing device.

Abstract: A technique performs the task of knowledge-graph completion in a manner that is both scalable and resource efficient. In some implementations, the technique identifies a source entity having a source-target relation that connects the source entity to a yet-to-be-determined target entity. The technique also identifies a source-entity data item that provides a passage of source-entity text pertaining to the source entity. The technique uses a machine-trained encoder model to map the source-entity data item to source-entity encoded information. The technique then predicts an identity of the target entity based on the source-entity encoded information, and based on predicate encoded information that encodes the source-target relation. In some implementations, the technique also predicts the target entity based on a consideration of one or more neighboring entities that are connected to the source entity and their respective source-to-neighbor relations.

Abstract: A system for detecting material accumulation relative to rotating ground-engaging tools includes an agricultural implement having a frame and first and second ground-engaging tools supported relative to the frame, with the first ground-engaging tool corresponding to a different tool type than the second ground-engaging tool. The system also includes first and second speed sensor configured to provide data indicative of the rotational speeds of the first and second ground-engaging tools, respectively. In addition, the system includes a computing system communicatively coupled to the first and second speed sensors.

Abstract: A method of interfacing a discrete digital workshop information system is provided, where the information system includes mutual integration among product lifecycle management (PLM), enterprise resource planning (ERP), a manufacturing execution system (MES), an energy management system (EMS), and a warehouse management system (WMS) of a finished product. A method of interfacing based on a data dictionary fusing different function datasets is proposed to solve the problems of a current discrete industry information system, such as single in function, a small amount of integrated information, a large number of “information islands” existing, incapable of achieving full-process informatization management and control, difficulty in product quality tracing and the like. By the method, it is possible to realize flexible production in a discrete manufacturing industry, precise management and control of a production process, significant improvement in product quality and significant reduction in operating costs.

Abstract: Methods, systems, and devices supporting configurable resistivities for lines in a memory device, such as access lines in a memory array are described. For example, metal lines at different levels of a memory device may be oxidized to different extents in order for the lines at different levels of the memory device to have different resistivities. This may allow the resistivity of lines to be tuned on a level-by-level basis without altering the fabrication techniques and related parameters used to initially form the lines at the different levels, which may have benefits related to at least reduced cost and complexity. Lines may be oxidized to a controlled extent using either a dry or wet process.

Abstract: A computing device is provided including a processor configured to execute a transformer including an encoder having a global layer configured to receive tokenized embeddings for each of a plurality of tokens in a local input sequence and compute a global self-attention vector for each of the tokenized embeddings. The encoder further includes a local layer configured to receive each global self-attention vector from the global layer and compute local self-attention for each local input sequence, and add and normalize the global self-attention vector with the local self-attention vector to thereby produce an encoder representation including a self-attention vector for each local input sequence that includes both global self-attention values and local self-attention values. The transformer is configured to output a prediction for the global input sequence based on the encoder representation of each of the local input sequences of the global input sequence.

Abstract: A system for detecting material accumulation relative to rotating ground-penetrating tools of an agricultural implement includes an agricultural implement having a frame and at least one rotating ground-penetrating tool supported relative to the frame. The rotating ground-penetrating tool(s) is configured to penetrate into the ground to a given penetration depth. The system also includes a tool speed sensor configured to provide data indicative of a rotational speed of the ground-penetrating tool(s) and a computing system communicatively coupled to the tool speed sensor. The computing system is configured to monitor the rotational speed of the ground-penetrating tool(s) based on the data provided by the tool speed sensor, determine a threshold rotational speed based at least in part on the penetration depth of the ground-penetrating tool(s), and determine when the rotational speed of the ground-penetrating tool(s) falls below the threshold rotational speed.

Abstract: The present invention discloses a right-angle turning method for small-diameter TBM exploration adit excavation and belongs to the field of geological exploration of water conservancy and hydropower projects.

Abstract: A query-processing technique includes an operation of matching the input query against a plurality of candidate target items, to produce a set of candidate query-item pairings. The matching is applicable to different classes of matching, but is performed by a computer processing architecture that uses a class-agnostic instance of query-processing logic and a class-agnostic target item index. After the matching operation, the technique assigns a matching class to each candidate query-item pairing in the set of candidate query-item pairings, to produce a set of classified pairings. The technique ultimately serves a particular output item to an end user, where the particular output item is chosen based on the results of the matching and assigning. Some implementations of the technique include a filtering operation whereby the candidate-item pairings are filtered to conform to a specified selection strategy or strategies. This filtering operation supplements or replaces the assigning operation.

Abstract: A training system produces a resource-efficient machine-trained model via a training architecture that employs plural processing paths. Some of the processing paths incorporate the use of auxiliary information that imparts external knowledge about source items being processed. The training architecture also employs contrastive learning that operates at different respective levels within the training architecture. For instance, the training architecture uses encoder-level contrastive learning to compare output information generated by different encoders within the training architecture. The training architecture uses decoder-level contrastive learning to compare output information produced by different decoders within the training architecture. An inference-stage system performs an application task using the model produced by the training system.

Abstract: Systems and methods are provided for learning classifiers for annotating a document with predicted labels under extreme classification where there are over a million labels. The learning includes receiving a joint graph including documents and labels as nodes. Multi-dimensional vector representations of a document (i.e., document representations) are generated based on graph convolution of the joint graph. Each document representation varies an extent of reliance on neighboring nodes to accommodate context. The document representations are feature-transformed using a residual layer. Per-label document representations are generated from the transformed document representations based on neighboring label attention. A classifier is trained for each of over a million labels based on joint learning using training data and the per-label document representation. The trained classifier performs highly efficiently as compared to other classifiers trained using disjoint graphs of documents and labels.