Abstract: Techniques are disclosed relating to automatically generating and analyzing database queries. In various embodiments, a database inquiry assistance system maintains a first machine learning model trained using query history data for a database and a second machine learning model using analysis history for the database. In an embodiment, the system receives from a user system a request for an inquiry into data stored in the database and identifies a sequence of queries for responding to the request, where identifying the sequence of queries includes applying the second machine learning model to the request. The system generates corresponding database query code for implementing one or more of the queries in the sequence of queries, where generating the corresponding database query code includes applying the first machine learning model to descriptors of one or more of the queries, and sends a plan identifying the sequence of queries to the user system.

Abstract: The invention proposes a system to compress reflected signals on a fluctuating noise background applied to active surveillance radar systems. This is a new, simple and effective solution to compress signals before sharing or transmitting to the processing center. Unlike previous systems based on performing compression on each reflected pulse, this transparent proposed system processes reflected regions in the form of a two-dimensional (2D) correlation matrix, combined with the dynamic calculation, automatically accumulates and adapts to changes; the convolution and compression algorithms are simple and effective since they are associated with the characteristics of active radar reflected areas in both frequency and time domains. Thanks to that, the system proposed in this invention provides effective and superior compression performance compared to the proposed systems.

Abstract: Methods and systems are presented for translating informal utterances into formal texts. Informal utterances may include words in abbreviation forms or typographical errors. The informal utterances may be processed by mapping each word in an utterance into a well-defined token. The mapping from the words to the tokens may be based on a context associated with the utterance derived by analyzing the utterance in a character-by-character basis. The token that is mapped for each word can be one of a vocabulary token that corresponds to a formal word in a pre-defined word corpus, an unknown token that corresponds to an unknown word, or a masked token. Formal text may then be generated based on the mapped tokens. Through the processing of informal utterances using the techniques disclosed herein, the informal utterances are both normalized and sanitized.

Abstract: Systems/techniques for facilitating context-enhanced category classification are provided. In various embodiments, a system can access a first textual description of a product or service. In various aspects, the system can identify, via execution of named entity recognition, one or more keywords in the first textual description. In various instances, the system can access, from a set of queryable databases, one or more second textual descriptions that respectively correspond to the one or more keywords. In various cases, the system can generate, via execution of word embedding, a first numerical representation of the first textual description and one or more second numerical representations of the one or more second textual descriptions. In various aspects, the system can identify, via execution of a machine learning classifier, a category label for the product or service, based on the first numerical representation and the one or more second numerical representations.

Abstract: Methods and systems are presented for translating informal utterances into formal texts. Informal utterances may include words in abbreviation forms or typographical errors. The informal utterances may be processed by mapping each word in an utterance into a well-defined token. The mapping from the words to the tokens may be based on a context associated with the utterance derived by analyzing the utterance in a character-by-character basis. The token that is mapped for each word can be one of a vocabulary token that corresponds to a formal word in a pre-defined word corpus, an unknown token that corresponds to an unknown word, or a masked token. Formal text may then be generated based on the mapped tokens. Through the processing of informal utterances using the techniques disclosed herein, the informal utterances are both normalized and sanitized.

Abstract: A compact integral conformal antenna system, and methods for fabricating the compact integral conformal antenna system, are described. Also described are use cases of the compact integral conformal antenna system in a wireless device which includes multiple multiple-input multiple-output (MIMO) and/or multi-user multiple-input multiple-output (MU-MIMO) capable radios.

Abstract: Methods and systems are presented for generating and using a machine learning model configured to perform cross-domain named entity recognition. The machine learning model is generated to accept a sentence associated with a target domain and to predict, for at least a word in the sentence, a corresponding entity associated with the target domain, without having been trained using training data associated with the target domain. In particular, the machine learning model is trained using only training data associated with a source domain. Based on derived relationships between entities associated with the source domain and entities associated with the target domain, the machine learning model is configured to transfer knowledge associated with the source domain to the target domain such that the machine learning model can map words within a sentence to entities associated with the target domain.

Abstract: A wireless-access point includes a first radio to transmit RF signals in a first channel; a second radio to transmit, simultaneously to transmissions of the RF signals by the first radio, RF signals in a second, non-overlapping channel; and a plurality of planar antennas coupled with corresponding first and second radios to receive the RF signals. First and second planar antennas are coupled with the first radio to receive therefrom, in the first channel, a first RF signal and a second RF signal, respectively. The first planar antenna is arranged with its normal along a first direction. The second planar antenna is arranged with its normal along a second, different direction. A third planar antenna is coupled with the second radio to receive therefrom a third RF signal in the second channel, the third planar antenna being arranged with its normal along a third direction.

Abstract: Techniques are disclosed relating to automatically generating and analyzing database queries. In various embodiments, a database inquiry assistance system maintains a first machine learning model trained using query history data for a database and a second machine learning model using analysis history for the database. In an embodiment, the system receives from a user system a request for an inquiry into data stored in the database and identifies a sequence of queries for responding to the request, where identifying the sequence of queries includes applying the second machine learning model to the request. The system generates corresponding database query code for implementing one or more of the queries in the sequence of queries, where generating the corresponding database query code includes applying the first machine learning model to descriptors of one or more of the queries, and sends a plan identifying the sequence of queries to the user system.

Abstract: Methods and systems are presented for translating informal utterances into formal texts. Informal utterances may include words in abbreviation forms or typographical errors. The informal utterances may be processed by mapping each word in an utterance into a well-defined token. The mapping from the words to the tokens may be based on a context associated with the utterance derived by analyzing the utterance in a character-by-character basis. The token that is mapped for each word can be one of a vocabulary token that corresponds to a formal word in a pre-defined word corpus, an unknown token that corresponds to an unknown word, or a masked token. Formal text may then be generated based on the mapped tokens. Through the processing of informal utterances using the techniques disclosed herein, the informal utterances are both normalized and sanitized.

Abstract: Systems and methods are presented for providing a response to a user query. Reception of a user query is detected. An augmentation machine learning model is utilized to determine one or more variations of the user query that correspond to a semantic meaning of the user query. A plurality of response candidates is determined that correspond to the user query by comparing the user query and the one or more variations of the user query to a plurality of documents. A final response candidate is determined from the plurality of response candidates based on utilizing a semantic machine learning model to perform a semantic comparison between the plurality of response candidates and at least the user query.

Abstract: A wireless networking infrastructure includes at least one multiple radio access point or base station including at least one radio that operates on a first radio communications protocol at a first frequency spectrum, and a second radio that operates on a second radio communication protocol at a second frequency spectrum; at least one remotely deployable wireless sensor; and a wireless network controller configured to establish connection with one or more of the at least one multiple radio access point or base station to form a wireless network operating at the first frequency spectrum, establish specific geolocations of the at least one multiple radio access point or base station and of the at least one remotely deployable wireless sensor, and receive and store radio operating parameters for radios operating at the first frequency spectrum at the specific geolocations.

Abstract: A wireless-access point includes a first radio to transmit RF signals in a first channel; a second radio to transmit, simultaneously to transmissions of the RF signals by the first radio, RF signals in a second, non-overlapping channel; and a plurality of planar antennas coupled with corresponding first and second radios to receive the RF signals. First and second planar antennas are coupled with the first radio to receive therefrom, in the first channel, a first RF signal and a second RF signal, respectively. The first planar antenna is arranged with its normal along a first direction. The second planar antenna is arranged with its normal along a second, different direction. A third planar antenna is coupled with the second radio to receive therefrom a third RF signal in the second channel, the third planar antenna being arranged with its normal along a third direction.

Abstract: An electronic apparatus includes a first housing configured to accommodate a first circuit and a first passage member for retracting a cable member, a second housing configured to accommodate a second circuit and a second passage member for retracting the cable member into the housing, a hinge structure provided between the first housing and the second housing and configured to rotatably connect the first housing and the second housing, a flexible display supported by the first housing and the second housing, and a cable member electrically connecting the first circuit and the second circuit via the first passage member, the hinge structure, and the second passage. At least one of the first passage member and the second passage member includes an elastic member including a hollow region penetrated by the cable member, and a bonding member filling between the cable member and the elastic member in the hollow region.

Abstract: Systems/techniques for facilitating context-enhanced category classification are provided. In various embodiments, a system can access a first textual description of a product or service. In various aspects, the system can identify, via execution of named entity recognition, one or more keywords in the first textual description. In various instances, the system can access, from a set of queryable databases, one or more second textual descriptions that respectively correspond to the one or more keywords. In various cases, the system can generate, via execution of word embedding, a first numerical representation of the first textual description and one or more second numerical representations of the one or more second textual descriptions. In various aspects, the system can identify, via execution of a machine learning classifier, a category label for the product or service, based on the first numerical representation and the one or more second numerical representations.

Abstract: An interface matrix arrangement for multi-beam, multi-port antenna is described.

Abstract: Methods and systems are presented for translating informal utterances into formal texts. Informal utterances may include words in abbreviation forms or typographical errors. The informal utterances may be processed by mapping each word in an utterance into a well-defined token. The mapping from the words to the tokens may be based on a context associated with the utterance derived by analyzing the utterance in a character-by-character basis. The token that is mapped for each word can be one of a vocabulary token that corresponds to a formal word in a pre-defined word corpus, an unknown token that corresponds to an unknown word, or a masked token. Formal text may then be generated based on the mapped tokens. Through the processing of informal utterances using the techniques disclosed herein, the informal utterances are both normalized and sanitized.

Abstract: Methods and systems are presented for generating and using a machine learning model configured to perform cross-domain named entity recognition. The machine learning model is generated to accept a sentence associated with a target domain and to predict, for at least a word in the sentence, a corresponding entity associated with the target domain, without having been trained using training data associated with the target domain. In particular, the machine learning model is trained using only training data associated with a source domain. Based on derived relationships between entities associated with the source domain and entities associated with the target domain, the machine learning model is configured to transfer knowledge associated with the source domain to the target domain such that the machine learning model can map words within a sentence to entities associated with the target domain.

Abstract: A wireless-access point includes a first radio to transmit RF signals in a first channel; a second radio to transmit, simultaneously to transmissions of the RF signals by the first radio, RF signals in a second, non-overlapping channel; and a plurality of planar antennas coupled with corresponding first and second radios to receive the RF signals. First and second planar antennas are coupled with the first radio to receive therefrom, in the first channel, a first RF signal and a second RF signal, respectively. The first planar antenna is arranged with its normal along a first direction. The second planar antenna is arranged with its normal along a second, different direction. A third planar antenna is coupled with the second radio to receive therefrom a third RF signal in the second channel, the third planar antenna being arranged with its normal along a third direction.

Abstract: A wireless-access point includes a first radio to transmit RF signals in a first channel; a second radio to transmit, simultaneously to transmissions of the RF signals by the first radio, RF signals in a second, non-overlapping channel; and a plurality of planar antennas coupled with corresponding first and second radios to receive the RF signals. First and second planar antennas are coupled with the first radio to receive therefrom, in the first channel, a first RF signal and a second RF signal, respectively. The first planar antenna is arranged with its normal along a first direction. The second planar antenna is arranged with its normal along a second, different direction. A third planar antenna is coupled with the second radio to receive therefrom a third RF signal in the second channel, the third planar antenna being arranged with its normal along a third direction.