Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may select information indicating a request or a notification that is independent of a request for an uplink grant. The UE may transmit the information in a message associated with a scheduling request. Numerous other aspects are described.

Abstract: Facilitating reduction of noise in non-standard printed circuit board assembly component descriptions using a zero-shot model to identify salient component class descriptions is presented herein.

Abstract: Implementations claimed and described herein provide systems and methods for determining driving attributes using telematics data. The systems and methods use telematics data generated via a telematics device disposed in a vehicle. One or more driving attributes associated with a vehicle operator and/or the vehicle based on the telematics data are determined by the system. The one or more driving attributes associated with the vehicle operator and/or the vehicle are denormalized and aggregated. Furthermore, the aggregated driving attributes are communicated with a provider computing device in response to a request for the aggregated driving attributes.

Abstract: A prognosis system is programmed to: obtain, from a biomarker image generation service, a set of original biomarker images, generate a set of synthetic images using the set of original biomarker images, perform an entropy calculation on each of the set of synthetic images to obtain a set of entropy images, apply an entropy thresholding on the set of entropy images to obtain a set of threshold images, apply a masking on the set of threshold images to obtain a pair of masked images for each of the set of threshold images, compare each of the pair of masked images using a cosine similarity (CS) value to assign a cancer value to a corresponding threshold image of the set of threshold images, and based on the cancer value, determine a cancer prognosis to each of the synthetic images.

Abstract: An apparatus comprises at least one processing device configured to receive a query to determine associations between named entities and aspect terms for a document, to generate, utilizing a first machine learning model, a first set of encodings classifying words of the document as being aspect or non-aspect terms, to generate, utilizing a second machine learning model, a second set of encodings classifying associations of the words, and to determine, for a given aspect term, attention weights for a given subset of the words surrounding the given aspect term. The processing device is also configured to generate, utilizing a third machine learning model, predictions of association between the given aspect term and named entities recognized in the given subset of the words, and to provide a response to the query comprising at least one of the predicted associations.

Abstract: A corpus of textual data records, labeled by experts as corresponding to a defined characteristic, that comprise descriptions of problems with an item are collected. A language model generates a plurality of n-grams from the corpus. Frequently occurring n-grams are analyzed using a zero-shot learning model to determine similarity to the defined characteristic. N-grams highly similar to the defined characteristic may be selected as defined phrases. N-grams highly similar to another characteristic may also be selected to reduce false positives. The zero-shot model may also be used to determine a weighting factor for each defined phrase for each record. A relevance score is determined for a record by multiplying the weighting factors for each phrase that has a similarity score relative to the record above a threshold based on the expert labeling. The relevancy score may be used to automatically diagnose problems with the item.

Abstract: Methods, apparatus, and processor-readable storage media for security-related image processing using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining image data associated with at least one user-provided component; obtaining identifier data associated with the at least one user-provided component; obtaining image data associated with at least one reference component from at least one database using at least a portion of the obtained identifier data; performing a comparison, using at least one pretrained computer vision model, of at least a portion of the obtained image data associated with the at least one user-provided component and at least a portion of the obtained image data associated with the at least one reference component; and performing one or more security-related actions based at least in part on results of the comparison.

Abstract: A system can receive first input data indicative of first user input identifying a first diagnostic state of a computing system. The system can, based on the first input data, determine a second diagnostic state of the computing system, the second diagnostic state comprising information of the first diagnostic state. The system can present a first indication of the second diagnostic state via a user interface. The system can receive second input data indicative of second user input confirming the second diagnostic state. The system can determine a recommended action based on the second diagnostic state. The system can present a second indication of the recommended action via the user interface.

Abstract: Facilitating reduction of noise in non-standard printed circuit board assembly component descriptions using a zero-shot model to identify salient component class descriptions is presented herein.

Abstract: At least one embodiment is directed to a computer-implemented method for using machine vision to categorize a locality to conduct product positioning analyses, the method including: generating locality profile scores for each locality of a plurality of localities using deep learning networks, where the locality profile score includes distributions of entity classes within the locality; extracting a set of entities having the same entity class from a group of localities; retrieving historical purchasing data for the entity set; and generating a sequence of products likely to be purchased by a target entity as a function of: the similarity of purchasing characteristics of the target entity with respect to other entities, product sequences found in product purchase of other entities, and entity profile weights extracted from the locality profile scores of other entities that have purchased one or more of the same products as the target entity.

Abstract: An apparatus comprises a processing device configured to obtain a first image, to detect one or more designated types of objects in the first image, at least a given one of the designated types of objects comprising at least a given portion of a computing device associated with a service request and, responsive to detecting the given one of the designated types of objects, to identify features of the given portion of the computing device in the first image. The processing device is also configured to perform an augmentation of the identified features of the given portion of the computing device to generate a second image, the second image comprising a modified version of the first image that contains the given portion of the computing device and excludes one or more other portions of the first image, and to process the service request utilizing the second image.

Abstract: A corpus of textual data records, labeled by experts as corresponding to a defined characteristic, that comprise descriptions of problems with an item are collected. A language model generates a plurality of n-grams from the corpus. Frequently occurring n-grams are analyzed using a zero-shot learning model to determine similarity to the defined characteristic. N-grams highly similar to the defined characteristic may be selected as defined phrases. N-grams highly similar to another characteristic may also be selected to reduce false positives. The zero-shot model may also be used to determine a weighting factor for each defined phrase for each record. A relevance score is determined for a record by multiplying the weighting factors for each phrase that has a similarity score relative to the record above a threshold based on the expert labeling. The relevancy score may be used to automatically diagnose problems with the item.

Abstract: One example method includes accessing input data elements from logs that identify user problems with computing system components, the data elements each associated with a respective original class label that identifies a class of computing system components to which the data element relates, the respective original class labels forming a group of class labels, and a first of the original class labels is overrepresented in the group, and reducing overrepresentation of the first original class label in the group by creating an arbitrary aggregation of some of the class labels that includes the first original class label. The method includes creating, based on a hierarchical modeling structure, prepared data in which an original class label is replaced by the aggregation. Next a hierarchical model and benchmark model are trained, and each model generates respective predictions for comparison. An inferencing process is performed to determine which predicted label will be used.

Abstract: An apparatus comprises a processing device configured to obtain a plurality of images each comprising at least a given portion of an instance of a computing device and to detect, utilizing an image classification machine learning model, at least a subset of the plurality of images having one or more designated failure characteristics. The processing device is also configured to create, utilizing a semantic segmentation machine learning model, a mask for each image in the subset of the plurality of images, the created masks characterizing locations of the designated failure characteristics in each image in the subset of the plurality of images. The processing device is further configured to generate an aggregated view of locations of the designated failure characteristics for the subset of the plurality of images utilizing the created masks, and to perform failure analysis for the computing device utilizing the generated aggregated view.

Abstract: An apparatus comprises at least one processing device configured to receive a query to perform sentiment analysis for a document, to generate, utilizing a first machine learning model, a first set of encodings classifying words of the document as being aspect or non-aspect terms, to generate, utilizing a second machine learning model, a second set of encodings classifying sentiment of the words of the document, and to determine, for a given aspect term, attention weights for a given subset of the words of the document surrounding the given aspect term. The processing device is also configured to generate, utilizing a third machine learning model, a given sentiment classification of the given aspect term based on the attention weights and a given portion of the second set of encodings for the given subset of the words, and to provide a response to the query comprising the given sentiment classification.

Abstract: A multimodal PET (positron emission tomography)/MRI (magnetic resonance imaging) contrast agent, a process of synthesizing said PET/MRI contrast agent, and a pharmaceutical formulation comprising said PET/MRI contrast agent are disclosed. The PET/MRI contrast agent comprises a magnetic signal generating core, and a coating portion formed at least partially over a surface of said magnetic signal generating core, wherein the coating portion comprises a plurality of layers, including an inner layer having a functionalized surface, and an outer layer in the form of a radionuclide electrolessly plated layer formed on said functionalized surface.

Abstract: An apparatus comprises at least one processing device configured to receive a query to generate a summary of a document, to perform two or more iterations of filtering the document to produce a current version of the summary of the document, wherein each of the iterations comprises determining similarity between a first vector representation of the current version of the summary and second vector representations of respective ones of two or more portions of the unstructured text data of the document not yet added to the current version of the summary. The processing device is also configured to generate, following identification of one or more designated stopping criteria in a given iteration, a final version of the summary based at least in part on the current version of the summary produced in the given iteration, and to provide a response to the query comprising the final version of the summary.

Abstract: An apparatus comprises at least one processing device configured to receive a query to perform sentiment analysis for a document, to generate, utilizing a first machine learning model, a first set of encodings classifying words of the document as being aspect or non-aspect terms, to generate, utilizing a second machine learning model, a second set of encodings classifying sentiment of the words of the document, and to determine, for a given aspect term, attention weights for a given subset of the words of the document surrounding the given aspect term. The processing device is also configured to generate, utilizing a third machine learning model, a given sentiment classification of the given aspect term based on the attention weights and a given portion of the second set of encodings for the given subset of the words, and to provide a response to the query comprising the given sentiment classification.

Abstract: An apparatus comprises at least one processing device configured to receive a query to determine associations between named entities and aspect terms for a document, to generate, utilizing a first machine learning model, a first set of encodings classifying words of the document as being aspect or non-aspect terms, to generate, utilizing a second machine learning model, a second set of encodings classifying associations of the words, and to determine, for a given aspect term, attention weights for a given subset of the words surrounding the given aspect term. The processing device is also configured to generate, utilizing a third machine learning model, predictions of association between the given aspect term and named entities recognized in the given subset of the words, and to provide a response to the query comprising at least one of the predicted associations.

Abstract: An apparatus comprises at least one processing device configured to receive a query to generate a summary of a document, to perform two or more iterations of filtering the document to produce a current version of the summary of the document, wherein each of the iterations comprises determining similarity between a first vector representation of the current version of the summary and second vector representations of respective ones of two or more portions of the unstructured text data of the document not yet added to the current version of the summary. The processing device is also configured to generate, following identification of one or more designated stopping criteria in a given iteration, a final version of the summary based at least in part on the current version of the summary produced in the given iteration, and to provide a response to the query comprising the final version of the summary.