Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment may generate a Layer 1 measurement report identifying one or more measurements by corresponding measurement indices, refrain from including a measurement value in the Layer 1 measurement report for at least one measurement, and transmit the Layer 1 measurement report. Numerous other aspects are provided.

Abstract: A computer system, computer program product and method for determining a probability of attaining a PK-PD target associated with efficacy for a patient that includes a processor obtaining information identifying an infection and based on the information, generating and displaying, by the one or more processors, a list comprising one or more pathogens consistent with the information, the processor then obtaining a first indication designating at least one pathogen from the list comprising one or more pathogens and based on at the obtaining of the least one pathogen, generating a list comprising one or more drug therapies utilized to treat the at least one pathogen.

Abstract: A method, apparatus, non-transitory computer readable medium, and system for document retrieval include obtaining a query and a document. A prompt generator generates a prompt for a reasoning model based on the query and the document. The reasoning model generates a reasoning result based on the prompt. In some cases, the reasoning result indicates that the document answers the query. A machine learning model provides the document in response to the query based on the reasoning result.

Abstract: Systems and methods directed to a service that analyzes media and outputs a confidence score of the media based on inconsistencies within media and differences between source media and the media, are described herein. In one aspect, a media file may be obtained at a computing resource service provider. The media file may be analyzed using at least one of a plurality of functions to generate media results that indicate inconsistencies within the media file, and/or differences between the media file and corresponding media registered with the computing resource service provider or contextual media that contains similar content to the media file, where individual functions of the plurality of functions are executed in parallel by at least one compute unit. A confidence score, which indicates a significance of the inconsistencies within the media file and/or differences from the registered or contextual media, may be generated based on the media results.

Abstract: A method for anomaly detection includes receiving an anomaly detection query from a user. The anomaly detection query requests data processing hardware determine one or more anomalies in a dataset including a plurality of examples. Each example in the plurality of examples is associated with one or more features. The method includes training a model using the dataset. The trained model is configured to use a local outlier factor (LOF) algorithm. For each respective example of the plurality of examples in the dataset, the method includes determining, using the trained model, a respective local deviation score based on the one or more features. The method includes determining that the respective local deviation score satisfies a deviation score threshold and, based on the location deviation score satisfying the threshold, determining that the respective example is anomalous. The method includes reporting the respective anomalous example to the user.

Abstract: Systems and methods for predicting relevance of resources to search queries. In some aspects, the system may receive a search query requesting resources from a database and may identify resources, including messages, relating to the search query. The system may extract, from the messages, hyperlinks specifying locations within the database. The system may input, into a model, the search query and the hyperlinks to cause the model to generate predictions of relevance of the hyperlinks to the search query. The system may then determine an overall relevance score for the resources in relation to the search query based on the predictions of relevance.

Abstract: Vector dataset index parameter determination is performed by obtaining a statistical variance of each vector dataset among a plurality of vector datasets, grouping the plurality of vector datasets into a plurality of vector dataset groups based on the statistical variance, determining, for each vector dataset group, build parameter values, obtaining the statistical variance of a target vector dataset, and applying, to the target vector dataset, the determined build parameter values corresponding to a vector dataset group selected based on the statistical variance.

Abstract: Vector dataset index parameter determination is performed by building an index of a vector dataset according to each build parameter group among a plurality of build parameter groups, generating search parameter groups, submitting each vector query in a vector query set to each index according to the candidate search parameter values from each search parameter group to obtain, for each submission, a result content and a result delay value, obtaining, for each submission, a vector distance between at least one result vector of the result content and the submitted vector query, determining a relative accuracy value for each submission by comparing the vector distance of the result content to the vector distance of at least one other submission, correlating build parameter values and search parameter values with the relative accuracy values and the result delay values, and fixing build parameter values based on the correlating.

Abstract: A compute instance stores a programmable feature extractor associated with a machine learning model maintained by a server-based computing system configured to communicate with the compute instance by way of a network. The machine learning model is based on a feature set that includes a plurality of features. The compute instance executes the programmable feature extractor to generate a feature vector corresponding to a data instance accessed by the compute instance, where the feature vector includes a feature value specific to the data instance for each feature included in the feature set. The compute instance transmits the feature vector corresponding to the data instance to the server-based computing system for use as a training input to the machine learning model.

Abstract: In some implementations, a server may receive, from a first user, an indication of a first set of times and receive, from the second user, an indication of a second set of times. The server may determine a first location associated with the first user and a second location associated with the second user. Accordingly, the server may identify a set of events based on the first location, the second location, the first set of times, and the second set of times. The server may further select a subset of events, from the set of events, based on first event data associated with the first user and second event data associated with the second user. Based on a selected event from the subset of events, the server may identify an API associated with the selected event and may trigger a call to the identified API.

Abstract: A method for performing a video content search is provided. The method includes receiving a search query associated with one or more videos and obtaining subtitle files associated with the one or more videos. The subtitle files each include a corresponding transcript having a plurality of transcript passages and video timestamps for the plurality of transcript passages. The method further includes determining query responses based on contents of video segments of the one or more videos. The video segments correspond to passage time ranges relating to the search query. The passage time ranges each include video timestamps associated with a transcript passage in the subtitle files that correlates with the search query. The method further includes causing a query result to be presented to a user on a graphical user interface, the query result being based on the determined query responses.

Abstract: Coordinating processing of audio queries is provided. A system receives a query. The system provides the query to a first digital assistant component and a second digital assistant component for processing. The system receives a first response to the query from the first digital assistant component, and a second response to the query from the second digital assistant component. The first digital assistant component can be authorized to access a database the second digital assistant component is prohibited from accessing. The system determines, based on a ranking decision function, to select the second response to the query from the second digital assistant component. The system provides, responsive to the selection, the second response from the second digital assistant to a computing device.

Abstract: Systems, methods, and computer-program products for entity resolution are disclosed. Entity resolution embodiments include receiving tree data from each of a pair of entities, extracting and/or aggregating feature vectors or metric functions therefrom, and generating similarity scores between the pair of entities. The similarity scores may be weighted using machine-learned weights. The weighted similarity scores are used to generate a combinatorial probability score accounting for combined likelihoods of field values between the pair of entities. A classification of the pair of entities is performed based on the combinatorial probability score, with a genealogical database modified based on the classification.

Abstract: The LOCATION-CONSCIOUS SOCIAL NETWORKING APPARATUSES, METHODS, AND SYSTEMS (“LCSN”) transforms user personality and location inputs via LCSN components into match and transaction outputs. LSCN may be a processor-implemented method for location-conscious social networking, involving collecting personality details pertaining to a user, analyzing the personality details and constructing a personality profile for the user. The LCSN may also collect geolocation information from the user and compare it to geolocation data from other users, may use user personality profiles to calculate a compatibility rating between the user and other users, and may send an alert to users if the geolocation data indicates that the user is within a specified distance of another user, and/or if the users are matches for each other. Users may further be able to express anonymous interest in other users by anonymously rating potential matches.

Abstract: Recombinant bacterial cells are provided that comprise a stable non-canonical amino acid translation pathway. In some aspects, the bacteria comprise nucleic acids encoding a non-canonical amino acid translation pathway (e.g., a tRNA for incorporation of a non-canonical amino acid, such selenocysteine); a marker polypeptide that includes the non-canonical amino acid. Recombinant tRNA and selection marker coding sequences are likewise provided.

Abstract: A system and method is provided for receiving, analyzing, organizing and presenting data stored on a mobile communication device. In one embodiment of the present invention, the data is organized by application-type and is searchable. In one embodiment, the system may be configured to generate first and second search results from a first search term entered by a reviewer, where the first search result includes at least a first record from a first application, the second search result includes at least a second record from a second application, and both records are displayed to the reviewer at the same time. In performing the search, the first record should match the first search term and the second record should match a second search term, where the second is identified by the system and is related to but different from the first search term.

Abstract: Some embodiments of the current disclosure disclose methods and systems for training for training a natural language processing intent classification model to perform few-shot classification tasks. In some embodiments, a pair of an utterance and a first semantic label labeling the utterance may be generated and a neural network that is configured to perform natural language inference tasks may be utilized to determine the existence of an entailment relationship between the utterance and the semantic label. The semantic label may be predicted as the intent class of the utterance based on the entailment relationship and the pair may be used to train the natural language processing intent classification model to perform few-shot classification tasks.

Abstract: A method includes a computing device verifying authenticity of a blockchain-encoded record representing a statement of words and an entigen group to produce an authenticity indicator where a set of identigens is determined utilizing a knowledge database for each word to produce sets of identigens and where the sets of identigens is interpreted to produce the entigen group. When the authenticity indicator indicates an authentic status, the method further includes interpreting, based on an updated knowledge database, updated sets of identigens to produce an updated entigen group. The method further includes updating the blockchain-encoded record to represent the statement and the updated entigen group to facilitate subsequent utilization of an updated validated interpretation of the statement as the updated entigen group.

Abstract: Methods, systems and programming for providing query suggestions based on user feedback. In one example, a prefix of a query is first received. An input including a prefix of a query is received from a user in a search session. A plurality of query suggestions are fetched based on the prefix of the query. Rankings of the plurality of query suggestions are determined based, at least in part, on the user's previous interactions in the search session with respect to at least one of the plurality of query suggestions. The at least one of the plurality of query suggestions has been previously provided to the user in the search session. The plurality of query suggestions are provided in the search session based on their rankings as a response to the input.

Abstract: Computer-implemented methods, systems, computing devices, and/or the like are provided. The computer-implemented method may include identifying a raw representation of the source data and a processed representation of the source data; selecting a read mode for reading the raw representation of the source data and the processed representation of the source data; selecting a write mode for writing the raw representation of the source data and the processed representation of the source data, wherein the write mode is based at least in part on the read mode; selecting a process mode for processing the source data, wherein the process mode is based at least in part on the write mode; selecting a representation mode for representing the processed source data; and storing the raw representation of the source data and the processed representation of the source data based at least in part on the representation mode.

Abstract: A method may include receiving data related to a plurality of items and processing the data using a machine learning model. The machine learning model may have been trained to output a score for each of the plurality of items based on one or more target variables and to process the data using a grouped linear regression for groups of items based on sub-divisions of the groups. The method may include storing the output in a data store. Each entry in the data store may include at least an item identifier for an item, a group name, and the score. The method may include receiving search criteria for a search and identifying a set of search results in a group of items. The method may include determining an order of the set of search results and outputting the set of search results.

Abstract: The present specification provides, amongst other things, a computing network computing optimization and method that can select and generate a digital redirect token for association with content that includes a proposed travel schema with an affiliation with the content.

Abstract: The present invention enables, according to a situation, accurate extraction of related expressions pertaining to search queries and question sentences. A related expression extraction device 1 receives input of text data, performs at least one of categorization of the received text data and determination of a structural pattern of the text data, determines, based on a result of at least one of the categorization of the text data and the determination of the structural pattern of the text data, which of a plurality of comparative assessment models 27 and 28 is used to extract related expression group data 26, and extracts a related expression related to content of the text data from the related expression group data 26 using the determined comparative assessment models 27 and 28.

Abstract: A method for detecting anomalous data is provided. The method comprises collecting a training dataset comprising a number of transactional time series, wherein the time series comprise non-anomalous data entries for a specified transaction type. The training dataset is fed into a gated recurrent unit (GRU) network, which learns the data distribution for the transactional time series. The GRU predicts expected future values of the specified transaction type according to the learned data distribution. An upper bound and a lower bound for future values are calculated based a standard deviation of the predicted values. When new data entries of the specified transaction type are received their values are compared to the upper bound and the lower bound, and an error notification is provided if the values of the new data entries fall outside the upper bound or lower bound.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for analyzing images for generating query responses. One of the methods includes determining, using a textual query, an image category for images responsive to the textual query, and an output type that identifies a type of requested content; selecting, using data that associates a plurality of images with a corresponding category, a subset of the images that each belong to the image category, each image in the plurality of images belonging to one of the two or more categories; analyzing, using the textual query, data for the images in the subset of the images to determine images responsive to the textual query; determining a response to the textual query using the images responsive to the textual query; and providing, using the output type, the response to the textual query for presentation.

Abstract: Aspects of the disclosure include methods and systems for dynamically creating tagged search results. An exemplary method includes identifying, responsive to receiving a search query, search results. The search results include a plurality of web page links to respective web pages. The method includes identifying, for each web page, one or more page features, generating a plurality of descriptive tags for the respective web pages, generating one or more page-tag pairs by pairing a web page of the respective web pages with a descriptive tag of the plurality of descriptive tags, and determining a relevance score for each page-tag pair. One or more descriptive tags having a relevance score above a predetermined threshold are selected. Tagged search results are provided that include the plurality of web page links and, for each web page link, the selected one or more descriptive tags for the respective web page.

Abstract: A system receives a media content item and analyzes the media content item to identify copyrighted material contained therein. Analyzing the media content item includes determining an initial interval for selecting segments of the media content item to be analyzed, generating a first digital fingerprint of a first segment of the media content item, comparing the first digital fingerprint to at least a first subset of a plurality of digital fingerprints of a first plurality of known works to determine whether the first digital fingerprint matches one of the plurality of digital fingerprints of the first plurality of known works, and determining an updated interval for selecting the segments of the media content item to be analyzed based on whether or not a match was found between the first digital fingerprint and one of the plurality of digital fingerprints of the first plurality of known works.

Abstract: A computing platform configured to (i) receive, via a user interface, a command to activate a bulk association mode for bulk associating an attribute value with one or more line items, (ii) based on the command, activate the bulk association mode and thereby display, via the user interface, a bulk association view comprising a target region for receiving an indication of one or more line items that are to be bulk associated with one or more given attribute values, (iii) receive first user input selecting at least two given attribute values for bulk association, (iv) receive second user input indicating one or more given line items that are to be bulk associated with the two given attribute values, and (v) after receiving the second user input, cause each given line item to be associated with the two given attribute values.

Abstract: A computer-implemented method for treating post-modeling data includes computing, sequentially for each category of a feature, a category importance (CI) value. The CI value is based on a model accuracy change when records of a category being examined are reassigned to a remaining set of categories of the feature according to a cumulative distribution of records among the remaining set of categories of the feature, wherein the remaining set of categories include all categories of the feature, except for the category being examined. A post-modeling category is performed to merge of each category having the CI value less than a CI value threshold.

Abstract: Systems and methods are provided to compare a target sample of text to a set of textual records, each textual record including a sample of text and an indication of one or more segments of text within the sample of text. Semantic similarity values between the target sample of text and each of the textual records are determined. Determining a particular semantic similarity value between the target sample of text and a particular textual record of the corpus includes: (i) determining individual semantic similarity values between the target sample of text and each of the segments of text indicated by the particular textual record, and (ii) generating the particular semantic similarity value between the target sample of text and the particular textual record based on the individual semantic similarity values. A textual record is then selected based on the semantic similarities.

Abstract: Electronic discovery using predictive filtering is disclosed herein. An example method includes providing a filtering interface that includes a field value input, a predicted values selector, and a predictor type selector; receiving at least a pivot selected from the field value input and a predicted value from the predicted values selector; searching a plurality of documents based on the pivot and the predicted value selected for any of predictive phrases or predictive concepts; calculating a predictive value for each of the predictive phrases or predictive concepts; and generating a graphical user interface that includes the predictive phrases or predictive concepts in conjunction with their respective predictive value.

Abstract: Techniques for generating opinion-based content responsive to a user input are described. The system may receive a user input, and determine dialog context data corresponding to a dialog between a user and the system, and including the user input. The system may determine generation of content responsive to the user input requires opinion-based knowledge, and may extract entities from the dialog context data, and determine natural language data of a knowledge base that includes entities similar to the extracted entities. The system may processes the natural language data and the dialog context data to determine a subset of the natural language data that is responsive to the user input. The system may generate output data responsive to the user input using the responsive natural language data and the dialog context.

Abstract: A system to generate and present an intelligent RSS feed that performs operations that include: accessing a media collection from a media host, wherein the media collection may comprise a podcast; caching a sequence of media objects associated with the media collection at a media repository; receiving a request that comprises an identifier associated with the media collection; inserting a media object at a position among the sequence of media objects; and causing display of a presentation of the sequence of media objects at a client device wherein the sequence of media objects includes one or more podcasts, and the presentation of the sequence of media objects includes the media object inserted at the position among the sequence of media objects.

Abstract: A computer-implemented method, system and computer program product for generating a visual transformation of a business analytic artifact. Features from a received business analytic artifact (e.g., dashboard) are extracted. Furthermore, constraints are determined for a transformed representation of the business analytic artifact. A heuristic-based search is then performed on a space of possible transformed representations of the business analytic artifact using the extracted features to identify a transformed version that satisfies the determined constraints and maximizes readability. A final score is then generated for each possible transformed representation of the business analytic artifact based on the extent that the transformed version satisfies the determined constraints and maximizes the readability. The transformed representation of the business analytic artifact that has the highest score is selected and displayed on the target device.

Abstract: A method of generating an instruction performance skeleton employs an instruction unit configured to receive a natural language instruction. From the natural language instruction, a sequence of clauses may be extracted. The instruction unit then determines a target website or websites on which to perform the task. The object models of the target website are generated. A comparison of the sequence of actions to the object model and its labelling hierarchical class structure is performed. Based on this comparison, an instruction performance skeleton is generated. In future, on the basis of a further natural language instruction that is similar to the previous natural language instruction, the instruction performance skeleton may be modified to generate a playback performance skeleton to arrange performance of a task.

Abstract: Systems and methods for automatically scoring ecosystem activity are disclose. The method includes receiving data from repositories and developer forums related to a software project, calculating a popularity score of the software project based on the received data, evaluating forks data of forks of the software project, calculating a project activity score of the software project based on the received data and the evaluated forks data, calculating a defect score, calculating a community score by evaluating posts of the developer forums on the software project, calculating a solution score by evaluating solutions provided as a response to queries on the software project, calculating a sentiment score by aggregating and analyzing identified positive and negative sentiments, calculating a userbase score, and calculating an ecosystem activity score based on one or more individual scores.

Abstract: A system serializing and deserializing models configured to (i) store a first model, wherein the first model includes a plurality of functionalities; (ii) generate a human-readable document based on the first model, wherein the human-readable document describes the first model; (iii) generate a second model based on the human-readable document, wherein the second model includes the plurality of functionalities; and (iv) perform at least one execution of the second model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating subqueries from a query. In one aspect, a method includes obtaining a query, generating a set of two subqueries from the query, where the set includes a first subquery and a second subquery, determining a quality score for the set of two subqueries, determining whether the quality score for the set of two subqueries satisfies a quality threshold, and in response to determining that the quality score for the set of two subqueries satisfies the quality threshold, providing a first response to the first subquery that is responsive to a first operation that receives the first subquery as input and providing a second response to the second subquery that is responsive to a second operation that receives the second subquery as input.

Abstract: Systems and methods are described for enhancing performance of a search engine by using pre-existing fine-tuned machine learning models that include domain-specific knowledge. A computing device receives search results from a search engine based on a query, in which the search results include chunk identifiers and a first weight score associated with each chunk identifier. The computing device further determines a subset of search results, which includes a first predetermined number of top-ranked chunk identifiers in the search results. The computing device generates, for each chunk identifier in the subset of search results, one or more second weight scores. Then the computing device generates, via a machine learning model, ensemble scores for the chunk identifiers. Afterwards, the computing device determines a second predetermined number of top-ranked chunk identifiers based on the ensemble scores.

Abstract: A computing server may continuously update a set of nodes that are addable to a data tree based on past interactions of the user with one or more nodes. The computing server may track a recently interacted set of interacted nodes with which the user has interacted within a number of past interactions. The computing server may select a pool of candidate nodes based on the recently interacted set. At least one of the candidate nodes is within a domain boundary of one of the interacted nodes that is in the recently interacted set. The domain boundary may be determined by the degree of relationship. The computing server may present one or more candidate nodes in the pool as a version of the continuously updated set of nodes. The computing server may update the pool of candidate nodes as additional interactions performed by the user updates the recently interacted set.

Abstract: Systems and methods are provided that include a processor executing a program to receive input text, divide the input text into sentences, identify one or a plurality of jurisdiction candidates in the sentences from a predetermined taxonomy to generate a jurisdictions list, transform the jurisdictions list using a type recognition neural network to disambiguate jurisdictions in the jurisdictions list, the type recognition neural network being trained on a labeled ground truth dataset containing pairs of geographic names and tax jurisdiction types, and generate and output the jurisdictions list as a jurisdiction prediction list.

Abstract: Machine-learning-aided automatic taxonomy for web data. In an embodiment, a training dataset of annotated features is used to train a model to predict a class in a taxonomy of web-based activities. The features may be derived from a uniform resource locator (URL) of an online resource and associated metadata. During operation, the features may be extracted from the URL and metadata of each activity record in web data. The trained model may be applied to the extracted features for each activity record to predict a class within the taxonomy. The predicted taxonomic class may be stored in association with the URL that was extracted from the activity record to produce a taxonomized URL.

Abstract: Techniques for predicting an answer to a question using a machine learning model are described. In some examples, the model predicts one or more answers to the question by: predicting at least two answers to the question using a first component of the question-answer model from a set of passages, generating, using a second component of the question-answer model, at least one question for each of the predicted at least two answers, and performing roundtrip predictions until each generated question only has one answer.

Abstract: Automatic spotlighting of participant video feeds in a video conference are based on one or more triggers detected in the participant video feeds. Participant video feeds are added to a spotlight video queue. The participant video feeds are elevated to an active spotlight status based on certain criteria. The participant video feeds that are elevated to the active spotlight status are displayed adjacent to a host video feed on a display.

Abstract: Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for providing content. Search results responsive to a query are identified including a first search result in a top set of search results, the first search result associated with a first entity. A first eligible content item is identified for presentation along with the search results, the first eligible content item associated with the first entity. A combined content item is identified that is a combination of the first search result and first eligible content item and is to be presented as a search result responsive to the query. The combined content item is augmented including: identifying entities related to the first entity, identifying content items that are associated with the related entities, selecting at least one identified content item, and using content from the selected content items to augment the combined content item.

Abstract: In some implementations, a system may determine, based on a qualification model, a prediction output of an analysis of user information. The system may determine, based on a generator model, a plurality of counterfactual explanations associated with the prediction output and the user information. The system may cluster, according to a clustering model, the plurality of counterfactual explanations into clusters of counterfactual explanations. The system may select, based on a classification model, a counterfactual explanation from a cluster of the clusters of counterfactual explanations. The system may provide a request for feedback associated with the counterfactual explanation. The system may receive feedback data associated with the request for feedback. The system may update a data structure associated with the clustering model based on the feedback data and the counterfactual explanation to form an updated data structure. The system may perform an action associated with the updated data structure.

Abstract: A client application sends an application programming interface call to an interface of a content management system. The call specifies one or more content item search criteria. Sending the call causes the content management system to perform a lookup in a content item index to identify at least one content item that satisfies the one or more content item search criteria. Based on sending the call, the client application receives from the content management system a suggestion to attach the at least one content item to a text being displayed by at the computing system.

Abstract: A media server machine may be configured to provide media content within a datastream. This datastream may be provided to a media device that is configured to present the media content on a display. Also, this datastream may contemporaneously contain an “app-sync indicator” for the media content. The app-sync indicator is a data structure that signals the media device to launch an application on a companion device. By providing the app-sync indicator contemporaneously with the media content in the datastream, the launching of the application on the companion device may be synchronized with the media content. The app-sync indicator may specify the application to be launched. Also, the app-sync indicator may specify supplemental content to be presented by the launched application on the companion device.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for content recommendation. A method for content recommendation includes determining a similarity between a first recommendation result and a second recommendation result for a content set. The first recommendation result and the second recommendation result are determined based on different recommendation techniques and respectively indicative of a recommendation degree for each content in the content set. The method further includes adjusting the second recommendation result using the similarity. In addition, the method further includes determining a target recommendation result for the content set based on the first recommendation result and the adjusted second recommendation result. In this manner, the accuracy and stability of the finally obtained recommendation results can be improved.

Abstract: A computer-implemented method is disclosed for generating structured data representations from unstructured input text sequences through a neural network-based extraction pipeline. The method involves acquiring input text sequences and processing them with an extraction neural network to produce output text sequences. Each output text sequence includes embedded delimiter tokens that annotate specific text segments, mapping them to semantic categories defined within a structured schema. The annotated text segments encapsulate semantically relevant information extracted from the input sequence. The output text sequences are subsequently processed to generate structured data records.