Abstract: A system for determining the importance of encoded image components for artificial intelligence tasks includes an image capture or storage unit, a processor and a communication interface. The processor can receive components of transformed domain image data from the one or more image capture or storage units across the communication interface. The processor can be configured to determine the relative importance of the components of the transformed domain image data for an artificial intelligence task.

Abstract: Discrete cosine transformation (DCT) information can be estimated from adjacent blocks of the same frame. DCT information can be estimated from different frames. Motion vectors can be used to track the position of objects in some frames of the video. For example, a stream of encoded frames is received; the encoded frames are entropy decoded and dequantized to produce DCT information for blocks of the frames; and DCT information for a block in a frame is determined using the DCT information produced from the entropy decoding and dequantizing for a different block.

Abstract: A method includes in response to a user selection of a command of a coding language, causing display of a set of argument blocks in a text input region based on syntax of the command. Each argument block allows the user to input a value of an argument of the command to the argument block. In response to a user selection to modify the set of argument blocks, an argument block is added to the set of argument blocks displayed in the text input region based on the syntax of the command. In response to receiving from the user the input of the value of the argument to the added argument block, the command is caused to be coded in the text input region with at least the argument having the value from the input to the added argument block.

Abstract: Methods and systems are provided for implementing training of learning models, including obtaining a pre-trained weight set for a learning model on a sample dataset and on a first loss function; selecting at least two tasks having heterogeneous features to be computed by a reference model; obtaining a reference dataset for the at least two tasks; designating a second loss function for feature embedding between the heterogeneous features of the at least two tasks; training the learning model on the first loss function and training the reference model on the second loss function, in turn; and updating the weight set based on a feature embedding learned by the learning model and a feature embedding learned by the reference model, in turn. Methods and systems of the present disclosure may alleviate computational overhead incurred by executing the learning model and loading different weight sets at a central network of the model.

Abstract: An input weight pattern of a machine learning model may be received. The input weight pattern may be pruned to produce an output weight pattern based on a predetermined pruning algorithm. The pruning algorithm may include partitioning the input weight pattern into a plurality of sub-patterns, each row of the input weight pattern including sub-rows of a first number of sub-patterns, and each column of the input weight pattern including sub-columns of a second number of sub-patterns; and pruning sub-columns and sub-rows from the plurality of sub-patterns to achieve predetermined column and row sparsities respectively, with a constraint that at least one sub-row in each row of the input weight pattern is not pruned. The output weight pattern may further be compressed to produce a compact weight pattern. The compact weight pattern has lower memory and computational overheads as compared to the input weight pattern for the machine learning model.

Abstract: An integrated circuit device includes a memory controller coupleable to a memory. The memory controller to schedule memory accesses to regions of the memory based on memory timing parameters specific to the regions. A method includes receiving a memory access request at a memory device. The method further includes accessing, from a timing data store of the memory device, data representing a memory timing parameter specific to a region of the memory cell circuitry targeted by the memory access request. The method also includes scheduling, at the memory controller, the memory access request based on the data.

Abstract: The present disclosure relates to systems and methods for providing vector-wise sparsity in neural networks. In some embodiments, an exemplary method for providing vector-wise sparsity in a neural network, comprises: dividing a matrix associated with the neural network into a plurality of vectors; selecting a first subset of non-zero elements from the plurality of vectors to form a pruned matrix; and outputting the pruned matrix for executing the neural network using the pruned matrix.

Abstract: Systems and methods include causing presentation of a first cluster in association with an event of the first cluster, the first cluster from a first set of clusters of events. Each event includes a time stamp and event data. Based on the presentation of the first cluster, an extraction rule corresponding to the event of the first cluster is received from a user. Similarities in the event data between the events are determined based on the received extraction rule. The events are grouped into a second set of clusters based on the determined similarities. Presentation is caused of a second cluster in association with an event of the second cluster, where the second cluster is from the second set of clusters.

Abstract: The present disclosure relates to systems and methods for providing block-wise sparsity in neural networks. In one implementation, a system for providing block-wise sparsity in a neural network may include at least one memory storing instructions and at least one processor configured to execute the instructions to: divide a matrix of weights associated with a neural network into a plurality of blocks; extract non-zero elements from one or more of the plurality of blocks; re-encode the extracted non-zero elements as vectors with associated coordinates of the extracted non-zero elements within the one or more blocks; enforce input sparsity in the neural network corresponding to the associated coordinates; and execute the neural network using the vectors and the enforced input sparsity.

Abstract: The present disclosure relates to systems and methods for dynamically executing sparse neural networks. In one implementation, a system for providing dynamic sparsity in a neural network may include at least one memory storing instructions and at least one processor configured to execute the instructions to: reduce an input vector and a set of weights of the neural network, execute an input layer of the neural network using the reduced input vector and set of weights to generate a reduced output vector; expand the reduced output vector to a full output vector using first predictable output neurons (PONs); using a PON map, reduce a dimension of the full output vector; execute subsequent layers of the neural network using the reduced full output vector to produce a second reduced output vector; and expand the second reduced output vector to a second full output vector using second PONs.

Abstract: Novel organosiloxane compounds are disclosed that are useful in firefighting foams. Methods of making and using the organosiloxane compounds and foams are also described.

Abstract: Novel organosiloxane compounds are disclosed that are useful in firefighting foams. Methods of making and using the organosiloxane compounds and foams are also described.

Abstract: Novel polyorganosiloxane compounds are disclosed that are useful in firefighting foams. Methods of making and using the polyorganosiloxane compounds and foams are also described.

Abstract: A method includes in response to a user selection of a command of a coding language, causing display of a set of argument blocks in a text input region based on syntax of the command. Each argument block allows the user to input a value of an argument of the command to the argument block. In response to a user selection to modify the set of argument blocks, an argument block is added to the set of argument blocks displayed in the text input region based on the syntax of the command. In response to receiving from the user the input of the value of the argument to the added argument block, the command is caused to be coded in the text input region with at least the argument having the value from the input to the added argument block.

Abstract: Various approaches for automating code completion are described herein. More particularly, approaches are provided that automatically generate coded commands of a coding language (i.e., code) that function and operate as intended by the user. As the user codes the commands, such approaches assist a user in various ways. For example, such automated assistance provides the user an understanding of various coding options available in the coding language. The assistance also enforces the proper employment of the available coding options, as well as provides an understanding of the functionality of the generated code. Automating code completion provides various benefits to the user, such as decreasing the time the user spends generating code, increasing the likelihood that the generated code functions and operates on a system as intended, and reducing the number of code versions required to be executed or compiled by the system.

Abstract: A method of manufacturing a lead frame, includes preparing a stamping arrangement to stamp a lead frame comprising a plurality of electrode contact regions, wherein complementary contact regions are separated by an initial gap width; and a number of connecting bars, wherein a connecting bar extends between regions of the lead frame; using the stamping arrangement to stamp the lead frame; and deforming at least one connecting bar of the stamped lead frame to reduce the gap width between complementary contact regions to a final gap width. Further described is a lead frame comprising a plurality of LED electrode contact regions, with a gap width of at most 250 ?m between complementary contact regions after the deformation step. The invention further describes an LED lighting device comprising such a lead frame and at least one LED die package mounted onto complementary electrode contact regions of the lead frame.

Abstract: The present invention relates to a multichip system and a method for scheduling threads in 3D stacked chip. The multichip system comprises a plurality of dies stacked vertically and electrically coupled together; each of the plurality of dies comprising one or more cores, each of the plurality of dies further comprising: at least one voltage violation sensing unit, the at least one voltage violation sensing unit being connected with the one or more cores of each die, the at least one voltage sensing unit being configured to independently sense voltage violation in each core of each die; and at least one frequency tuning unit, the at least one frequency tuning unit being configured to tune the frequency of each core of each die, the at least one frequency tuning unit being connected with the at least one voltage violation sensing unit. The multichip system and method described in present invention have many advantages, such as reducing voltage violation, mitigating voltage droop and saving power.

Abstract: Aqueous film forming firefighting composition concentrates are provided that contain an effective amount of a monomeric zwitterionic or anionic C6 perfluoroalkyl surfactant having a molecule weight less than 800 daltons. The compositions also contain an effective amount of a foam stabilizing agent, and an effective amount of at least one non-fluorinated surfactant. The composition has less than 0.8% F, and is substantially free of any surfactant containing a perfluoroalkyl group containing more than 6 carbon atoms. The composition meets Military Specification MIL-F-24385F.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of obtaining a uniform resource locator (URL) of a first webpage that is shown on a graphical user interface and that is external to a website of a retailer (where, in some embodiments, the URL is obtained from a referral website or is entered by a user from a chat window or search box), using a web scraper to extract web text displayed on the first webpage on the graphical user interface, processing the web text displayed on the first webpage on the graphical user interface to determine an interest of a user, using a set of rules to determine items related to the web text displayed on the first webpage on the graphical user interface, and coordinating displaying the items on a second webpage to promote the items as related to the interest of the user, where the second webpage is internal or external

Abstract: Embedded Performance Monitoring is an “out-of-box” approach for performance monitoring of a DBMS. Performance monitoring of a DBMS is achieved through use of a browser to access the DBMS once a DBMS is installed and configured. The approach exploits a DBMS configured with the native capability to provide performance monitoring data and software via a browser. To retrieve and use the performance monitoring data from the DBMS, a browser executes software downloaded to the browser from the DBMS using web-based technologies.