Abstract: Reconfigurable antenna systems with ground tuning pads are provided herein. In certain configurations, an antenna system includes an antenna element, a first tuning conductor spaced apart from the antenna element on a first side of the antenna element, a second tuning conductor spaced apart from the antenna element on a second side of the antenna element, a first ground tuning pad configured to receive a ground voltage, and a first switch electrically connected between the first tuning conductor and the first ground tuning pad. The first switch is operable to selectively connect the first tuning conductor to the first ground tuning pad to thereby tune the antenna element.

Abstract: Techniques for using enhanced logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system and inputting the utterance into a machine-learning model including a series of network layers. A final network layer of the series of network layers can include a logit function. The machine-learning model can map a first probability for a resolvable class to a first logit value using the logit function. The machine-learning model can map a second probability for a unresolvable class to an enhanced logit value. The method can also include the chatbot system classifying the utterance as the resolvable class or the unresolvable class based on the first logit value and the enhanced logit value.

Abstract: The present disclosure is related to techniques for converting a natural language utterance to a logical form query and deriving a natural language interpretation of the logical form query. The techniques include accessing a Meaning Resource Language (MRL) query and converting the MRL query into a MRL structure including logical form statements. The converting includes extracting operations and associated attributes from the MRL query and generating the logical form statements from the operations and associated attributes. The techniques further include translating each of the logical form statements into a natural language expression based on a grammar data structure that includes a set of rules for translating logical form statements into corresponding natural language expressions, combining the natural language expressions into a single natural language expression, and providing the single natural language expression as an interpretation of the natural language utterance.

Abstract: Techniques for using logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system. The chatbot system can input the utterance into a machine-learning model including a set of binary classifiers. Each binary classifier of the set of binary classifiers can be associated with a modified logit function. The method can also include the machine-learning model using the modified logit function to generate a set of distance-based logit values for the utterance. The method can also include the machine-learning model applying an enhanced activation function to the set of distance-based logit values to generate a predicted output. The method can also include the chatbot system classifying, based on the predicted output, the utterance as being associated with the particular class.

Abstract: The headset includes an electro-acoustic transducer, at least one noise reduction processing chain including external and internal microphones, a processing filter including mounted in parallel between the external microphone and an adder, at least one elementary filter each including a recursive filter and an elementary variable gain amplifier, a control unit for the gain of each elementary amplifier according to a signal coming from the external microphone and an error signal coming from the internal microphone. The processing filter includes an open-loop main filter connected at the input to the external microphone and the output of which is connected, to the adder, and to the input of the or each elementary filter.

Abstract: Disclosed are systems, methods, and articles for determining compatibility of a mobile application and operating system on a mobile device. In some aspects, a method includes receiving one or more data values from a mobile device having a mobile medical software application installed thereon, the data value(s) characterizing a version of the software application, a version of an operating system installed on the mobile device, and one or more attributes of the mobile device; determining whether the mobile medical software application is compatible with the operating system by at least comparing the received data value(s) to one or more test values in a configuration file; and sending a message to the mobile device based on the determining, the message causing the software application to operate in one or more of a normal mode, a safe mode, and a non-operational mode.

Abstract: Methods and systems for attaching a radiopaque marker to a prosthetic heart valve to indicate a location of a commissure of the prosthetic heart valve are disclosed. As one example, a prosthetic heart valve includes a frame including a plurality of struts forming a plurality of cells of the frame arranged between an inflow end and an outflow end of the frame, a plurality of leaflets arranged within the frame, and at least one commissure comprising an attachment member arranged across a selected cell of the plurality of cells of the frame and commissure tabs of two adjacent leaflets coupled to the attachment member. The valve further includes a radiopaque marker arranged on the attachment member of the commissure within the selected cell.

Abstract: Techniques are disclosed herein for addressing catastrophic forgetting and over-generalization while training a model to transform natural language to a logical form such as a meaning representation language. The techniques include accessing training data comprising natural language examples, augmenting the training data to generate expanded training data, training a machine learning model on the expanded training data, and providing the trained machine learning model. The augmenting includes (i) generating contrastive examples by revising natural language of examples identified to have caused regression during training of a machine learning model with the training data, (ii) generating alternative examples by modifying operators of examples identified within the training data that belong to a concept that exhibits bias, or (iii) a combination of (i) and (ii).

Abstract: Techniques are disclosed herein for using named entity recognition to resolve entity expression while transforming natural language to a meaning representation language. In one aspect, a method includes accessing natural language text, predicting, by a first machine learning model, a class label for a token in the natural language text, predicting, by a second machine-learning model, operators for a meaning representation language and a value or value span for each attribute of the operators, in response to determining that the value or value span for a particular attribute matches the class label, converting a portion of the natural language text for the value or value span into a resolved format, and outputting syntax for the meaning representation language. The syntax comprises the operators with the portion of the natural language text for the value or value span in the resolved format.

Abstract: Systems and methods fine-tune a pretrained machine learning model. For a model having multiple layers, an initial set of configurations is identified, each configuration establishing layers to be frozen and layers to be fine-tuned. A configuration that is optimized with respect to one or more parameters is selected, establishing a set of fine-tuning layers and a set of frozen layers. An input for the model is provided to a remote system. An output of the set of frozen layers of the model, given the provided input, is received back and locally stored. The set of fine-tuning layers of the model is loaded from the remote system. The model is fine-tuned by retrieving the locally stored output of the set of frozen layers, and updating weights associated with the set of fine-tuning layers of the machine learning model.

Abstract: Techniques are disclosed for augmenting training data for training a machine learning model to generate database queries. Training data comprising a first training example comprising a first natural language utterance, a logical form for the first natural language utterance, and associated first metadata is obtained. From the first training example, a template utterance is generated. A second natural language utterance is generated by filling slots in the template utterance based on a database schema and database values. Updated metadata is produced based on the first metadata and the second natural language utterance. A second training example is generated, comprising the second natural language utterance, the logical form for the first natural language utterance, and the updated metadata. The training data is augmented by adding the second training example. A machine learning model is trained to generate a database query comprising the database operation using the augmented training data set.

Abstract: Techniques are disclosed herein for converting a natural language utterance to an intermediate database query representation. An input string is generated by concatenating a natural language utterance with a database schema representation for a database. Based on the input string, a first encoder generates one or more embeddings of the natural language utterance and the database schema representation. A second encoder encodes relations between elements in the database schema representation and words in the natural language utterance based on the one or more embeddings. A grammar-based decoder generates an intermediate database query representation based on the encoded relations and the one or more embeddings. Based on the intermediate database query representation and an interface specification, a database query is generated in a database query language.

Abstract: Systems and methods identify whether an input utterance is suitable for providing to a machine learning model configured to generate a query for a database. Techniques include generating an input string by concatenating a natural language utterance with a database schema representation for a database; providing the input string to a first machine learning model; based on the input string, generating, by the first machine learning model, a score indicating whether the natural language utterance is translatable to a database query for the database and should be routed to a second machine learning model, the second machine learning model configured to generate a query for the database based on the natural language utterance; comparing the score to a threshold value; and responsive to determining that the score exceeds the threshold value, providing the natural language utterance or the input string to the second machine learning model.

Abstract: Techniques are disclosed herein for calibrating confidence scores of a machine learning model trained to translate natural language to a meaning representation language. The techniques include obtaining one or more raw beam scores generated from one or more beam levels of a decoder of a machine learning model trained to translate natural language to a logical form, where each of the one or more raw beam scores is a conditional probability of a sub-tree determined by a heuristic search algorithm of the decoder at one of the one or more beam levels, classifying, by a calibration model, a logical form output by the machine learning model as correct or incorrect based on the one or more raw beam scores, and providing the logical form with a confidence score that is determined based on the classifying of the logical form.

Abstract: A door assembly for a seat unit provided within a vehicle cabin, in particular an aircraft cabin, the door assembly comprising at least a fixed base structure, a door element movably mounted on the base structure and which is movable between a fully retracted position and at least one deployed position, and a door slide device provided between the base structure and the door element to movably support the door element on the base structure. Further, the door slide device comprises at least a slide carrier bracket fixed to the base structure or the door element, at least one slide unit which is coupled to the slide carrier bracket in a normal operation mode, and at least one separate auxiliary slide arrangement.

Abstract: Techniques are provided for using context tags in named-entity recognition (NER) models. In one particular aspect, a method is provided that includes receiving an utterance, generating embeddings for words of the utterance, generating a regular expression and gazetteer feature vector for the utterance, generating a context tag distribution feature vector for the utterance, concatenating or interpolating the embeddings with the regular expression and gazetteer feature vector and the context tag distribution feature vector to generate a set of feature vectors, generating an encoded form of the utterance based on the set of feature vectors, generating log-probabilities based on the encoded form of the utterance, and identifying one or more constraints for the utterance.

Abstract: The present disclosure relates to techniques for identifying out-of-domain utterances.

Abstract: Disclosed are systems, methods, and articles for determining compatibility of a mobile application and operating system on a mobile device. In some aspects, a method includes receiving one or more data values from a mobile device having a mobile medical software application installed thereon, the data value(s) characterizing a version of the software application, a version of an operating system installed on the mobile device, and one or more attributes of the mobile device; determining whether the mobile medical software application is compatible with the operating system by at least comparing the received data value(s) to one or more test values in a configuration file; and sending a message to the mobile device based on the determining, the message causing the software application to operate in one or more of a normal mode, a safe mode, and a non-operational mode.

Abstract: The present disclosure relates to techniques for identifying out-of-domain utterances.

Abstract: Systems and methods for scheduling and calendaring are provided in various embodiments. Systems, methods and software for accessing two or more electronic or online calendars for scheduling systems to allow for efficient identification and collection of available times and dates, without accessing or using any private or other information from the calendars. Systems, methods and software to also collect available times across multiple calendars, identify available overlapping open times and produce a set of available times and dates for an event across the various calendars, with then the option to schedule the event during one of the available times. Secure multiparty computation is applied to maintain privacy and security while allowing for the access, identification and collection of open times across each of various calendars or scheduling systems.