Abstract: Methods, systems, and computer-readable storage media for a ML system that reduces a number of target items from consideration as potential matches to a query item using token embeddings and a search tree.

Abstract: Methods, systems, and computer-readable storage media for a ML system that reduces a number of target items from consideration as potential matches to a query item using token embeddings and a search tree.

Abstract: Methods, systems, and computer-readable storage media for training a global matching ML model using a set of enterprise data associated with a set of enterprises, receiving a subset of enterprise data associated with an enterprise that is absent from the set of enterprises, fine tuning the global matching ML model using the subset of enterprise data to provide a fine-tuned matching ML model. deploying the fine-tuned matching ML model for inference, receiving feedback to one or more inference results generated by the fine-tuned matching ML model, receiving synthetic data from a LLM system in response to at least a portion of the feedback, and fine tuning one or more of the global matching ML model and the fine-tuned ML model using the synthetic data.

Abstract: Methods, systems, and computer-readable storage media for receiving, by an entity matching ML model, a query and target pair including a query entity and a target entity, providing, by the entity matching ML model, a query-target prediction by processing the query entity and the target entity, the query-target prediction indicating a match type between the query entity and the target entity, generating a prompt by populating a prompt template with at least a portion of the query-target prediction, inputting the prompt into a large language model (LLM), and receiving, from the LLM, an explanation that is responsive to the prompt and that describes one or more reasons for the query-target prediction output by the entity matching ML model.

Abstract: Methods, systems, and computer-readable storage media for receiving a real data table, providing a synthetic structured table based on the real data table, providing a sampled data table comprising a sub-set of real data of the real data table, transmitting a prompt to a LLM system, the prompt being generated based on the real data table and the synthetic structured data table, receiving synthetic unstructured data from the LLM system, providing an aggregate synthetic table that includes at least a portion of the synthetic unstructured data, and training a ML model using the aggregate synthetic table.

Abstract: Device and method for testing automation applications in a manner that improves the testing of the automation application, wherein data of an automation language for a test environment is processed, where the test environment includes a test environment for the actual system of the automation application and/or for at least one simulation of the automation application, and where at least one test of the test environment is performed such that. It is that the improved testing advantageous enables a safer automation solution.

Abstract: A molding tool (1000), in particular a thermoforming tool, for producing a container (10) is provided. The molding tool comprises a mold bottom (200) and a mold insert (100) receiving the mold bottom (200), wherein the mold insert (100) is constructed of at least two mold insert parts (110, 120) that together with the mold bottom (200) define a cavity (300) provided for reshaping a two-dimensional web of material into a container, wherein a first mold insert part (110) is mounted stationary in the molding tool (1000) and a second mold insert part is axially movable in the molding tool (1000).

Abstract: A method and control system for controlling an apparatus or system, wherein at least one safety function is provided with regard to the control of the apparatus or system, where the at least one safety function has at least one safety sub-function, the control system comprises a first safety-oriented controller and a second safety-oriented controller, the first safety-oriented controller and the second safety-oriented controller are communicatively coupled via a safety-oriented communication link, the first safety-oriented controller is configured to perform the at least one safety function, and the second safety-oriented controller function as a processor and is configured to perform the at least one safety sub-function using input data received via the safety-oriented communication link from the first safety-oriented controller.

Abstract: Disclosed herein are system, method, and computer program product embodiments for dropping or replacing data from datasets and training ML models to avoid overfitting in training data. An embodiment operates by generating a first set of data, wherein the first set of data may include a first plurality of entities. The first set of data may be modified by processing the first set of data, which results in a second set of data. The second set of data may include a second plurality of entities. The second set of data may be extracted to be used in a machine learning (ML) process based at least in part on at least one ML model. The second set of data may be trained on at least one ML model. A third set of data may be predicted based on the at least one ML model. The third set of data may include a third plurality of entities. The first, second, and third plurality of entities may be classified by a class.

Abstract: The present disclosure features methods of detecting crop damage in an algal culture. Such methods include detecting one or more carotenoids as a volatile organic compound in a sample obtained from a headspace of the algal culture.

Abstract: An electrode module for a redox flow cell comprises a frame having a peripheral seal which is arranged on an inner periphery of the frame and which has at least two inwardly directed elastic sealing lips. A peripheral groove is formed between two of the sealing lips. The electrode module also comprises an electrode having an outer periphery with which the electrode is seated in the groove of the seal.

Abstract: Methods, systems, and computer-readable storage media for a machine learning (ML) system for matching a query entity to one or more target entities, the ML system that reducing a number of query-target entity pairs from consideration as potential matches during inference.

Abstract: Methods, systems, and computer-readable storage media for a ML system that reduces a number of target items from consideration as potential matches to a query item using token embeddings and a search tree.

Abstract: Computer-readable media, methods, and systems are disclosed for feature attribution in a machine learning model. Samples may be generated for a machine learning model based on a normalized probability distribution. The samples may be used to determine a weight for features and feature pairs for the machine learning model. The weights of the features and feature pairs may be used to determine which features are significant for predictions within the machine learning model.

Abstract: Methods, systems, and computer-readable storage media for receiving a set of inference results generated by a ML model, the inference results including a set of query entities and a set of target entities, each query entity having one or more target entities matched thereto by the ML model, processing the set of inference results to generate a set of matched sub-sets of target entities by executing a search over target entities in the set of target entities based on constraints, for each problem in a set of problems, providing the problem as a tuple including an index value representative of a target entity in the set of target entities and a value associated with the query entity, the value including a constraint relative to the query entity, and executing at least one task in response to one or more matched sub-sets in the set of matched sub-sets.

Abstract: Methods, systems, and computer-readable storage media for receiving a set of inference results generated by a ML model, the inference results including a set of query entities and a set of target entities, each query entity having one or more target entities matched thereto by the ML model, processing the set of inference results to generate a set of matched sub-sets of target entities by executing a search over target entities in the set of target entities based on constraints, for each problem in a set of problems, providing the problem as a tuple including an index value representative of a target entity in the set of target entities and a value associated with the query entity, the value including a constraint relative to the query entity, and executing at least one task in response to one or more matched sub-sets in the set of matched sub-sets.

Abstract: Pairwise entity matching systems and methods are disclosed herein. A deep learning model may be used to match entities from separate data tables. Entities may be preprocessed to fuse textual and numeric data early in the neural network architecture. Numeric data may be represented as a vector of a geometrically progressing function. By fusing textual and numeric data, including dates, early in the neural network architecture the neural network may better learn the relationships between the numeric and textual data. Once preprocessed, the paired entities may be scored and matched using a neural network.

Abstract: Pairwise entity matching systems and methods are disclosed herein. A deep learning model may be used to match entities from separate data tables. Entities may be preprocessed to fuse textual and numeric data early in the neural network architecture. Numeric data may be represented as a vector of a geometrically progressing function. By fusing textual and numeric data, including dates, early in the neural network architecture the neural network may better learn the relationships between the numeric and textual data. Once preprocessed, the paired entities may be scored and matched using a neural network.

Abstract: A method for monitoring acceleration of a number A of axes of a multi-axis kinematic system utilizes a sampling process with a first sampling interval, wherein a first acceleration limit value assigned to the first sampling interval and a second different acceleration limit value is determined for the acceleration, where a second time interval is assigned to the second acceleration limit value, a plurality of position values of the axis is determined by sampling with the first sampling interval, a current acceleration is calculated via the ascertained position values, and the calculated current acceleration is monitored via a first instance of monitoring utilizing the first acceleration limit value and the assigned first sampling interval and, simultaneously, via a second instance of monitoring utilizing the second acceleration limit value and the assigned second time interval, such that acceleration of an axis is monitored using at least two acceleration limit values simultaneously.

Abstract: A vehicle roof for a motor vehicle having a roof opening which is defined by a peripheral vehicle roof edge, a moveable roof element for either closing or at least partially exposing the roof opening, which is held on the vehicle roof, and a continuous profiled seal fixed to the vehicle roof along the vehicle roof edge, wherein the continuous profiled seal comprises a first tubular seal, wherein the first tubular seal is designed to seal the vehicle roof in relation to the moveable roof element, the first tubular seal has a drainage lip, which extends downwards in a vertical direction (Z) from the first tubular seal.