Abstract: Systems and methods for language augmented video editing are disclosed. A method includes presenting a video editing assistant (e.g., via a communicatively coupled display and/or speaker). The method includes, in response to receiving a request from a user to create adaptive video content that satisfies a set of characteristics identified based on the request i) analyzing, using a first machine-learning model, existing video content to identify portions of the existing video content that satisfy the set of characteristics and ii) for each portion of the existing video content that satisfies the set of characteristics, create adaptive video content using a respective portion of the existing video content that satisfies the set of characteristics. The method includes generating, using a second machine-learning model, descriptions of the adaptive video content and presenting the adaptive video content and the descriptions of the adaptive video content.

Abstract: Aspects of the disclosure provide for congestion control mechanisms to reduce data transmission queues and to increase link utilization through precise congestion signals and reduction of control loop delay. A congestion control system (CC) system can utilize Back-To-Sender (BTS) packets over (round trip time) RTT control loop delay to react to congestion faster. The CC system can apply Proactive Ramp-up (PRU) to identify flow completions to occupy released bandwidth right on time, e.g., as bandwidth is made available. The CC system can perform supply matching (SM) through network calculus concepts to increase link utilization. The CC system can apply some or all of the use of BTS packets, PRU, and/or SM to reduce network latency and improve data flow completion time as compared with other approaches.

Abstract: Disclosed embodiments relate to systems, methods, and computer readable storage media for performing dataset discovery. Some embodiments may include accessing a data repository having a plurality of tables having cell values arranged in one or more columns and one or more rows, generating serialized sequences of the cell values that correspond to particular columns of the plurality of tables, inputting the serialized sequences into a natural language model, converting, using the natural language model, the serialized sequences into contextualized embeddings associated with the plurality of tables, storing the contextualized embeddings associated with the plurality of tables in one or more vector indices, receiving a query table, or generating an output of one or more candidate tables from the plurality of tables that are unionable with the received query table.

Abstract: Disclosed embodiments relate to extracting classification information from input text.

Abstract: The technology is directed to the use of a bitmap generated at a receiver to track the status of received packets sent by a transmitter. The technology may include a network device including an input port, output port, and circuitry. The circuitry may generate a transmitter bitmap that tracks each data packet sent to another network device. The circuitry of the network device may receive, from the other network device, a receiver bitmap that identifies each data packet that is received and not received from the network device. The circuitry may then determine which data packets to retransmit by comparing the transmitter bitmap to the receiver bitmap.

Abstract: A device may obtain first information relating to one or more first lung nodules identified in first imaging of a chest of a patient and second information relating to one or more second lung nodules identified in second imaging of the chest of the patient. The device may provide the first information and the second information to a machine learning model. The device may determine, using the machine learning model, a risk of lung cancer associated with the patient based on an elapsed time between performance of the first imaging and the second imaging and differences between the first information and the second information. The risk of lung cancer may have an inverse correlation to the elapsed time and a direct correlation to the differences. The device may perform one or more actions based on the risk of lung cancer that is determined.

Abstract: Disclosed embodiments relate to extracting classification information from input text.

Abstract: Disclosed embodiments relate to data management of entity pairs. Techniques can include receiving at least two sets of data and a data management task request with each including a set of entities. Techniques can determine a location of each entity in received data sets in a representative space by determining representative structure of the set of entities. Techniques can then for an entity, a set of representative entity pairs from each set of the at least two sets of data based on how close they are in the representative space. Technique can then analyze the set of representative entity pairs to identify most similar entity pairs include in a set of candidate pairs by determining closeness of location of entities in each entity pair in the representative space. Technique can then determine matched entity pairs of the candidate pairs using a first machine learning model is trained using the candidate pairs by applying labels, and utilizing the matched pairs to perform the requested data management task.

Abstract: A device may obtain first information relating to one or more first lung nodules identified in first imaging of a chest of a patient and second information relating to one or more second lung nodules identified in second imaging of the chest of the patient. The device may provide the first information and the second information to a machine learning model. The device may determine, using the machine learning model, a risk of lung cancer associated with the patient based on an elapsed time between performance of the first imaging and the second imaging and differences between the first information and the second information. The risk of lung cancer may have an inverse correlation to the elapsed time and a direct correlation to the differences.

Abstract: A method and an apparatus for detecting bending stiffness and a method for testing a display panel. The method for detecting bending stiffness includes: arranging an object to be tested on a reference surface to make a stationary portion and a rotating portion of the object to be tested attached to the reference surface, the stationary portion and the rotating portion being connected to each other; driving the rotating portion to bend from the reference surface toward the stationary portion, and acquiring a rotation angle between the rotating portion and the reference surface; acquiring a bending force received by the rotating portion when the rotating portion is bent from the reference surface toward the stationary portion; and determining bending stiffness of the object to be tested on the basis of the bending force and the rotation angle.

Abstract: Disclosed embodiments relate to natural language processing. Techniques can include receiving input text, extracting, from the input text, at least one modifier and aspect pair, receiving data from a knowledgebase, based on the at least one modifier and aspect pair and commonsense data, generate one or more premise embeddings, convert the input text into tokens, generating at least one vector for one or more of the tokens based on an analysis of the tokens, combine the at least one vector with the one or more premise embeddings to create at least one combined vector, and analyze the at least one combined vector wherein the analysis generates an output indicative of a feature of the input text.

Abstract: The technology is directed to the use of a bitmap generated at a receiver to track the status of received packets sent by a transmitter. The technology may include a network device including an input port, output port, and circuitry. The circuitry may generate a transmitter bitmap that tracks each data packet sent to another network device. The circuitry of the network device may receive, from the other network device, a receiver bitmap that identifies each data packet that is received and not received from the network device. The circuitry may then determine which data packets to retransmit by comparing the transmitter bitmap to the receiver bitmap.

Abstract: Disclosed embodiments relate to generalized entity matching. Techniques can include receiving a data pair of two entities that may be pre-processed to have parsable data structures, and serializing the data pair into a sequence of tokens based on data structure of each entity in the data pair. Techniques can further include encoding the serialized data pair to include topic attributes that may be mapped to data in the data pair and the topic of the mapped data matches the topic represented by topic attribute and the data in the data pair is concatenated. Techniques can further include pooling attributes in the data pair based on contextualized attributed representations of each encoded entity in the data pair and schema of each entity of the data pairs, where the contextual attribute representations are based on a first token of each encoded attribute in the sequence of tokens, and predicting matching labels between the data pairs based on pooled attributes.

Abstract: A system is provided for synchronizing clocks. The system includes a plurality of devices in a network, each device having a local clock. The system is configured to synchronize the local clocks according to a primary spanning tree, where the primary spanning tree has a plurality of nodes connected through a plurality of primary links, each node of the plurality of nodes representing a respective device of the plurality of devices. The system is also configured to compute a backup spanning tree before a failure is detected in the primary spanning tree, wherein the backup spanning tree includes one or more backup links that are different from the primary links. As such, upon detection of a failure in the primary spanning tree, the system reconfigures the plurality of devices such that clock synchronization is performed according to the backup spanning tree.

Abstract: An aspect of the disclosed technology is a computing system that implements a congestion control (CC) protocol that exploits and extends in-network telemetry (INT) to address, for example, blind spots typically found in end-to-end algorithms, determines CC for an actual bottleneck hop, realizes low queuing delay, and/or realizes convergence to network-wide max-min fair bandwidth allocation.

Abstract: Aspects of the disclosure provide for congestion control mechanisms to reduce data transmission queues and to increase link utilization through precise congestion signals and reduction of control loop delay. A congestion control system (CC) system can utilize Back-To-Sender (BTS) packets over (round trip time) RTT control loop delay to react to congestion faster. The CC system can apply Proactive Ramp-up (PRU) to identify flow completions to occupy released bandwidth right on time, e.g., as bandwidth is made available. The CC system can perform supply matching (SM) through network calculus concepts to increase link utilization. The CC system can apply some or all of the use of BTS packets, PRU, and/or SM to reduce network latency and improve data flow completion time as compared with other approaches.

Abstract: Disclosed embodiments relate to natural language processing. Techniques can include receiving input text, extracting, from the input text, at least one modifier and aspect pair, receiving data from a knowledgebase, based on the at least one modifier and aspect pair and commonsense data, generate one or more premise embeddings, convert the input text into tokens, generating at least one vector for one or more of the tokens based on an analysis of the tokens, combine the at least one vector with the one or more premise embeddings to create at least one combined vector, and analyze the at least one combined vector wherein the analysis generates an output indicative of a feature of the input text.

Abstract: Disclosed embodiments relate to natural language processing. Techniques can include receiving input text, extracting, from the input text, at least one modifier and aspect pair, receiving data from a knowledgebase, based on the at least one modifier and aspect pair and commonsense data, generate one or more premise embeddings, convert the input text into tokens, generating at least one vector for one or more of the tokens based on an analysis of the tokens, combine the at least one vector with the one or more premise embeddings to create at least one combined vector, and analyze the at least one combined vector wherein the analysis generates an output indicative of a feature of the input text.

Abstract: A system is provided for synchronizing clocks. The system includes a plurality of devices in a network, each device having a local clock. The system is configured to synchronize the local clocks according to a primary spanning tree, where the primary spanning tree has a plurality of nodes connected through a plurality of primary links, each node of the plurality of nodes representing a respective device of the plurality of devices. The system is also configured to compute a backup spanning tree before a failure is detected in the primary spanning tree, wherein the backup spanning tree includes one or more backup links that are different from the primary links. As such, upon detection of a failure in the primary spanning tree, the system reconfigures the plurality of devices such that clock synchronization is performed according to the backup spanning tree.

Abstract: Disclosed embodiments relate to generating training data for a machine learning model. Techniques can include accessing a machine learning model from a machine learning model repository and identifying a data set associated with the machine learning model. The identified data set is utilized to generate a set of data augmentation operators. The data augmentation operators applied on a selected sequence of tokens associated with the machine learning model to generate sequences of tokens. A subset of sequences of tokens are selected and stored in a training data repository. The stored sequences of tokens are provided to the machine learning model as training data.