Abstract: A method for making a component for use in a semiconductor processing chamber is provided. A component body is formed from a conductive material having a coefficient of thermal expansion of less than 10.0×10?6/K. A metal oxide layer is then disposed over a surface of the component body.

Abstract: A system and method for training an artificial intelligence engine for real-time monitoring to eliminate false positives is disclosed. The system includes at least one processor, a communication interface coupled to the processor, and a memory device storing executable code. Executing the executable code causes the processor to receive data from an AI security model, receive data from a false positive database, and correlate both sets of data. The correlated data is used to generate a training dataset and a test dataset used to train a false positive identification model. After evaluating the false positive identification model, an AI engine is applied to user registration. The AI engine includes an AI security model and the false positive identification model. Additionally, a system for evaluating the security of user registration utilizing the false positive identification model is disclosed.

Abstract: A system for determining vehicle location information includes a receiver supported on a first vehicle for receiving a communication from a second vehicle and a signal from each of a plurality of satellites. A detector is configured to detect a positional relationship between the first vehicle and the second vehicle. A processor is configured to determine a location of the first vehicle from received satellite signals, a location of the second vehicle based on the communication received from the second vehicle, a corrected location of the first vehicle based on the location of the second vehicle and the positional relationship between the vehicles, and a corrective mapping of a plurality of sections of a field of view of the receiver. Each section has ad correction factor for correcting a subsequently determined location of the first vehicle based on satellite signals received from satellites appearing in the respective sections.

Abstract: A system for guiding interactions with a user device requests a response from a plurality of users, stores the response as response data forming a subset of a personal data set of each of the responding users, and generates a predictive model during training of a machine learning program utilizing at least one neural network with a training data set including the personal data set of each of the plurality of users. The predictive model predicts a probability of a first one of the users associated with the user device interacting with a first product and/or service by correlating a personal data set of the first one of the users to the personal data set of at least a second one of the users and sends a communication relating to the first product and/or service to the user device when the predicted probability meets or exceeds a threshold value.

Abstract: An illustrative example embodiment of a method of processing information regarding movement of a first vehicle based on information regarding movement of at least one second vehicle includes: receiving a communication from the second vehicle that includes information regarding movement of the second vehicle, determining that the movement of the second vehicle corresponds to movement of the first vehicle, identifying at least one feature of the movement of the second vehicle that indicates an environmental condition that affects the movement of the second vehicle, identifying a sensor indication of movement of the first vehicle that corresponds to the feature of the movement of the second vehicle, and excluding the identified sensor indication from information used to determine movement of the first vehicle.

Abstract: Embodiments are disclosed for crash detection on one or more mobile devices (e.g., smartwatch and/or smartphone. In some embodiments, a method comprises: detecting a crash event on a crash device; extracting multimodal features from sensor data generated by multiple sensing modalities of the crash device; computing a plurality of crash decisions based on a plurality of machine learning models applied to the multimodal features, wherein at least one multimodal feature is a rotation rate about a mean axis of rotation; and determining that a severe vehicle crash has occurred involving the crash device based on the plurality of crash decisions and a severity model.

Abstract: The present invention relates to a composition comprising proteins identified in barn dust extract or peptides derived from one of the proteins. The composition is useful in the prevention or treatment of a disease.

Abstract: A mobile imaging system or mini C-arm with a variable aperture assembly is disclosed. The mini C-arm includes a detector and a moveable source. The aperture assembly being operatively coupled to the moveable source. The aperture assembly including a plurality of independently controllable blades such as, for example, first, second, third, and fourth blades, to define a variable aperture through which an X-ray beam is passed from the source to the detector. In one embodiment, each of the plurality of blades is independently controlled. In one embodiment, the aperture assembly includes a PCB sensor aligned with the plurality of blades for detecting a position of each of the plurality of the blades.

Abstract: A clock data recovery circuit includes a deglitch filter circuit and a timer circuit. The deglitch filter circuit is configured to remove pulses of less than a particular duration from a data signal to produce a deglitched data signal. The timer circuit is coupled to the deglitch filter, and is configured to compare a duration of a pulse of the deglitched data signal to a threshold duration, and identify the pulse as representing a logic one based on the duration of the pulse exceeding the threshold duration.

Abstract: A system and method for training an artificial intelligence engine for real-time monitoring to eliminate false positives is disclosed. The system includes at least one processor, a communication interface coupled to the processor, and a memory device storing executable code. Executing the executable code causes the processor to receive data from an AI security model, receive data from a false positive database, and correlate both sets of data. The correlated data is used to generate a training dataset and a test dataset used to train a false positive identification model. After evaluating the false positive identification model, an AI engine is applied to user registration. The AI engine includes an AI security model and the false positive identification model. Additionally, a system for evaluating the security of user registration utilizing the false positive identification model is disclosed.

Abstract: Various embodiments of the present disclosure describe holistic machine learning model evaluation techniques. The techniques include determining a holistic evaluation vector for a target machine learning model based on a plurality of evaluation scores for the target machine learning model. The plurality of evaluation scores may include a data evaluation score corresponding to a training dataset for the target machine learning model, a model evaluation score corresponding to one or more performance metrics for the target machine learning model, and a decision evaluation score corresponding to an output class of the target machine learning model. A holistic evaluation score for the target machine learning model may be determined from the holistic evaluation vector or a plurality of evaluation scores. An informed evaluation output is provided based on the holistic vector or score.

Abstract: Various embodiments of the present disclosure describe data evaluation techniques that leverage a graph-based machine learning model to evaluate a knowledge graph. The techniques include using a target graph model to generate a predictive representation for a graph node of a graph training dataset. The techniques include using a feature prediction model to generate predicted feature values for the graph node based on the predictive representation. The techniques include generating a data evaluation score for the graph training dataset based on the predicted feature values. The techniques include using the target graph model to generate a predictive output for the graph node based on the predictive representation and then generating an evaluation output for the target graph model based on the evaluation score and the predictive output.

Abstract: Various embodiments of the present disclosure describe machine learning monitoring and retraining techniques for automatically triggering model retraining based on evaluation scores. The techniques include receiving a request to process an input data object with a target machine learning model that is previously trained using an at least partially synthetic training dataset. The techniques include identifying a synthetic data object from the training dataset that corresponds to the input data object and, in response, modifying a holistic evaluation score for the model, initiating the performance of a labeling process for assigning a ground truth label to the input data object, and augmenting a supplemental training dataset with the input data object and the ground truth label. In the event that the holistic evaluation score decreased beyond a threshold, the model may be retrained with the supplemental training dataset.

Abstract: In one aspect, a computerized method for dynamically determining a veteran status of a candidate in a set of candidate search results, comprising: generating a searchable online database of diverse candidates; providing the searchable online database of diverse candidates qualified for a specified set of specialized and skilled positions, wherein a job title of candidate is associated with each candidate, and wherein a veteran's status probability is associated with each of the candidates; dynamically determining the veteran's status probability of each candidate in the online database by: parsing the candidate profiles to obtain a set of profile content for each candidate profile in the searchable online database of diverse candidates; matching the set of profile content with a set of veteran-status related keywords; based on a specified number of matches between the set of veteran-status related keywords and the profile content, calculating a probability that each candidate has a veteran status.

Abstract: A message generating system disseminates to each of multiple user devices first information items each associated with at least a respective one of multiple services made available by a first entity. When access indicative signals are received via a network connection, each prompted by a respective one of the user devices accessing at least one of the first information items, the system stores a respective awareness-stage record for each received access indicative signal, thereby recording that the respective user entity accessed at least one of the first information items. For each user entity for which a respective awareness-stage record is stored, a second item is generated and sent including information of a particular service associated with at least one first information item. User entities for which a second item was not generated are excluded as having not accessed a first item. The exclusion reduces data traffic on the network connection.

Abstract: A system for guiding interactions with a user device requests a response from first users, stores the response as response data forming a subset of a personal data set of each of the responding first users, and generates a predictive model during training of a machine learning program with a training data set including the personal data set of each of the first users. The predictive model predicts a predicted response of a second user associated with the user device by correlating a personal data set of the second user to the personal data set of at least one of the first users. The computer sends a communication to the user device of the second user having content relating to the first product and/or service when it is determined that the predicted response indicates an interest in or preference of the second user for the first product and/or service.

Abstract: A system for guiding interactions with a user device requests a response from a plurality of users, stores the response as response data forming a subset of a personal data set of each of the responding users, and generates a predictive model during training of a machine learning program utilizing at least one neural network with a training data set including the personal data set of each of the plurality of users. The predictive model predicts a probability of a first one of the users associated with the user device interacting with a first product and/or service by correlating a personal data set of the first one of the users to the personal data set of at least a second one of the users and sends a communication relating to the first product and/or service to the user device when the predicted probability meets or exceeds a threshold value.

Abstract: A system for guiding interactions with a user device requests a response from a plurality of users, stores the response as response data forming a subset of a personal data set of each of the responding users, and generates a predictive model during training of a machine learning program utilizing at least one neural network with a training data set including the personal data set of each of the plurality of users. The predictive model predicts a probability of a first one of the users associated with the user device interacting with a first product and/or service by correlating a personal data set of the first one of the users to the personal data set of at least a second one of the users and sends a communication relating to the first product and/or service to the user device when the predicted probability meets or exceeds a threshold value.

Abstract: A message generating system disseminates to each of multiple user devices first information items each associated with at least a respective one of multiple services made available by a first entity. When access indicative signals are received via a network connection, each prompted by a respective one of the user devices accessing at least one of the first information items, the system stores a respective awareness-stage record for each received access indicative signal, thereby recording that the respective user entity accessed at least one of the first information items. For each user entity for which a respective awareness-stage record is stored, a second item is generated and sent including information of a particular service associated with at least one first information item. User entities for which a second item was not generated are excluded as having not accessed a first item. The exclusion reduces data traffic on the network connection.

Abstract: A system and method for training an artificial intelligence engine for real-time monitoring to eliminate false positives is disclosed. The system includes at least one processor, a communication interface coupled to the processor, and a memory device storing executable code. Executing the executable code causes the processor to receive data from an AI security model, receive data from a false positive database, and correlate both sets of data. The correlated data is used to generate a training dataset and a test dataset used to train a false positive identification model. After evaluating the false positive identification model, an AI engine is applied to user registration. The AI engine includes an AI security model and the false positive identification model. Additionally, a system for evaluating the security of user registration utilizing the false positive identification model is disclosed.