Abstract: A method comprises collecting message-oriented-middleware system parameters from a plurality of message-oriented-middleware clusters, analyzing the parameters using one or more machine learning algorithms, and predicting, based at least in part on the analyzing, at least one anomaly in a message-oriented-middleware cluster of the plurality of message-oriented-middleware clusters. In the method, message metadata is collected from the message-oriented-middleware cluster, and at least part of the message metadata is transmitted to one or more remaining ones of the plurality of message-oriented-middleware clusters. At least the part of the message metadata corresponds to messaging operations to be transferred from the message-oriented-middleware cluster to the one or more remaining ones of the plurality of message-oriented-middleware clusters.

Abstract: A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about at least one of respective protocols and respective interfaces associated with respective ones of the plurality of components. The data is analyzed to determine at least one of the respective protocols and the respective interfaces associated with the respective ones of the plurality of components. In the method, operations of one or more components of the plurality of components are tested based at least in part on the determination of the at least one of the respective protocols and the respective interfaces. The method further includes outputting respective statuses of the one or more components, wherein the respective statuses are derived at least in part from the testing.

Abstract: Disclosed are articles comprising substrate and graphene coating that are configured to support a sample for electron or optical microscopy. Also disclosed are methods of making the same and methods of using the same in imaging technology.

Abstract: Techniques for machine learning (ML) and natural language processing (NLP) are described. One technique enables the creation of a clean training dataset through just a few API calls. Another technique provides an automated process for generating a domain-specific lexicon, which is then used to generate ML training datasets, in a manner that requires little to no human labor. Another technique gathers ML training data from domain-specific public sources, which are more likely than typical public sources to contain focused terminology and to be free from errors, thus resulting in trained ML models that provide more accurate inferences.

Abstract: Methods, apparatus, and processor-readable storage media for implementing topology explorers for message-oriented middleware using machine learning techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to at least one messaging topology associated with at least one message-oriented middleware; predicting one or more anomalies associated with the at least one messaging topology by processing at least a portion of the obtained data using a first set of one or more machine learning techniques; recommending one or more alternate messaging topologies associated with the at least one message-oriented middleware by processing at least a portion of the one or more predicted anomalies and at least a portion of the obtained data using a second set of one or more machine learning techniques; and performing one or more automated actions based on the one or more predicted anomalies and/or the one or more recommended alternate messaging topologies.

Abstract: A determination is made that an explanatory data set for a common set of predictions generated by a machine learning model for records containing text tokens is to be provided. Respective groups of related tokens are identified from the text attributes of the records, and record-level prediction influence scores are generated for the token groups. An aggregate prediction influence score is generated for at least some of the token groups from the record-level scores, and an explanatory data set based on the aggregate scores is presented.

Abstract: In some implementations, an emitter assembly includes a vertical cavity surface emitting laser (VCSEL) chip including a plurality of VCSELs in a bottom-emitting configuration, and multiple VCSELs, of the plurality of VCSELs, may be grouped in a cluster. The emitter assembly may include a carrier, and the VCSEL chip may be in a flip chip configuration with the carrier. The emitter assembly may include a conductive pillar electrically connected to the multiple VCSELs grouped in the cluster.

Abstract: In some implementations, an emitter assembly includes a vertical cavity surface emitting laser (VCSEL) chip including a plurality of VCSELs. The emitter assembly may include a plurality of conductive pillars electrically connected to the VCSEL chip. The emitter assembly may include a dummy pillar, electrically isolated from the VCSEL chip, mating with a slot. The VCSEL chip may include one of the dummy pillar or the slot.

Abstract: In some implementations, an emitter assembly includes a vertical cavity surface emitting laser (VCSEL) chip including a plurality of VCSELs respectively associated with a plurality of first electrical contacts. A first spacing of the plurality of first electrical contacts may define a first pitch. The emitter assembly may include a redistribution layer, disposed on the VCSEL chip, to increase the first pitch of the plurality of first electrical contacts. The emitter assembly may include a carrier having a plurality of second electrical contacts. A second spacing of the plurality of second electrical contacts may define a second pitch greater than the first pitch. The emitter assembly may include a plurality of conductive pillars that electrically connect the plurality of first electrical contacts and the plurality of second electrical contacts via the redistribution layer. The plurality of conductive pillars may be arranged according to the second spacing.

Abstract: In some implementations, an emitter assembly includes a vertical cavity surface emitting laser (VCSEL) chip including a plurality of VCSELs. The emitter assembly may include a plurality of conductive pillars electrically connected to the VCSEL chip, and a conductive pillar, of the plurality of conductive pillars, may have a solder cap at an end of the conductive pillar. The emitter assembly may include a pin extending into the solder cap.

Abstract: A determination is made that an explanatory data set for a common set of predictions generated by a machine learning model for records containing text tokens is to be provided. Respective groups of related tokens are identified from the text attributes of the records, and record-level prediction influence scores are generated for the token groups. An aggregate prediction influence score is generated for at least some of the token groups from the record-level scores, and an explanatory data set based on the aggregate scores is presented.

Abstract: Methods, apparatus, and processor-readable storage media for implementing extensible upgrade and modification as a service are provided herein. An example computer-implemented method includes processing one or more modifiers, wherein each modifier includes an independent processing unit having a given canonical structure and is configured to execute one or more automated actions related to at least one of application modification and application migration; obtaining data pertaining to multiple applications across multiple computing environments; determining, based at least in part on processing at least a portion of the obtained data, at least one of the one or more modifiers applicable for use in executing at least one of the one or more automated actions in connection with at least a portion of the multiple applications; and executing the at least one of the one or more automated actions using the at least one determined modifier.

Abstract: Provided is a method and system for building a compliance software service using reusable and configurable components. In one example, the method may include receiving a request to build a software in association with an identified jurisdiction from among a plurality of jurisdictions, retrieving a plurality of configurable software components which comprise built-in functionality that is generic across the plurality of jurisdictions, dynamically configuring non-generic functionality for the identified jurisdiction within the plurality of configurable software components based on inputs received from a user, and creating a software program for the identified jurisdiction based on the dynamically configured software components and storing a file including the created software program in a storage device.

Abstract: An L-shape design of battery blocks (100) is described. Within each pair of the battery blocks (100), one L-shape battery block (100-1) may be placed at an angle of 180° to another battery block (100-2). A sufficient non-linear gap may be left within each pair of battery blocks (100) for natural cooling through movement of air. A bus-bar (200) developed by twisting of multiple pure copper wires may be used for connecting the battery blocks (100) with nuts and bolts. A fuse wire is used to connect at least one of the terminals of the cells (102) to the bus-bar (200). The battery blocks (100) may be placed in trays (300) having transparent walls (302) made of plexiglass. The battery blocks (100) may be packed together using a Velcro® and/or tension wrapped with wire before being placed in the trays (300).

Abstract: A method including receiving, by a server computer, a request message from a token requestor computer on behalf of a user device. The request message comprising a first current token tracking value and a first function index value. The server computer can determine a second function index value. The server computer can then compare the first function index value to the second function index value. If the first function index value and the second function index value match, the server computer can determine a function based on the first function index value and a stored function table associated with the user device. The server computer can then determine a second current token tracking value based on the function, then compare the first current token tracking value to the second current token tracking value. The server computer can generate a response message in response to the comparing.

Abstract: A method comprises collecting message-oriented-middleware system parameters from a plurality of message-oriented-middleware clusters, analyzing the parameters using one or more machine learning algorithms, and predicting, based at least in part on the analyzing, at least one anomaly in a message-oriented-middleware cluster of the plurality of message-oriented-middleware clusters. In the method, message metadata is collected from the message-oriented-middleware cluster, and at least part of the message metadata is transmitted to one or more remaining ones of the plurality of message-oriented-middleware clusters. At least the part of the message metadata corresponds to messaging operations to be transferred from the message-oriented-middleware cluster to the one or more remaining ones of the plurality of message-oriented-middleware clusters.

Abstract: A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about at least one of respective protocols and respective interfaces associated with respective ones of the plurality of components. The data is analyzed to determine at least one of the respective protocols and the respective interfaces associated with the respective ones of the plurality of components. In the method, operations of one or more components of the plurality of components are tested based at least in part on the determination of the at least one of the respective protocols and the respective interfaces. The method further includes outputting respective statuses of the one or more components, wherein the respective statuses are derived at least in part from the testing.

Abstract: A method includes receiving a natural language query requesting data from a message oriented middleware infrastructure comprising a plurality of message oriented middleware providers, and analyzing the natural language query to determine one or more types of the data being requested. In the method, one or more stored queries corresponding to the determined one or more types of the data are identified. The one or more stored queries are in native command formats corresponding to respective ones of the plurality of message oriented middleware providers. The method also includes executing the identified one or more stored queries in the native command formats to retrieve the data from the plurality of message oriented middleware providers, and providing a response to the natural language query based on the retrieved data to a user via a user interface.

Abstract: A method including receiving, by a server computer, a request message from a token requestor computer on behalf of a user device. The request message comprising a first current token tracking value and a first function index value. The server computer can determine a second function index value. The server computer can then compare the first function index value to the second function index value. If the first function index value and the second function index value match, the server computer can determine a function based on the first function index value and a stored function table associated with the user device. The server computer can then determine a second current token tracking value based on the function, then compare the first current token tracking value to the second current token tracking value. The server computer can generate a response message in response to the comparing.

Abstract: A method includes receiving a natural language query requesting data from a message oriented middleware infrastructure comprising a plurality of message oriented middleware providers, and analyzing the natural language query to determine one or more types of the data being requested. In the method, one or more queries corresponding to the determined one or more types of the data are dynamically generated. The one or more queries are in native command formats corresponding to respective ones of the plurality of message oriented middleware providers. The method also includes executing the one or more queries in the native command formats to retrieve the data from the plurality of message oriented middleware providers, and providing a response to the natural language query based on the retrieved data to a user via a user interface.