Abstract: In an embodiment, a correlated forecast is computer generated by a processor that receives input data for historic values of a first input variable, creates forecast data for future values of the first input variable using the historic values of the first input variable, generates diagnostic data based on a diagnostic analysis of the forecast data, creates a first diagnostic variable that includes a first diagnostic value from a first cognitive process, generates a feature vector based on a second cognitive process that determines the feature vector by identifying a correlation between the first diagnostic variable and a second diagnostic variable, and generates a final forecast using the feature vector as an input for a cognitive forecasting process, where the first cognitive process determines the first diagnostic value based on the diagnostic data.

Abstract: A method, computer system, and a computer program product for content navigation within a media player is provided. The present invention may include displaying, by a computing device, a media content and a corresponding textual content. The present invention may include receiving, from a user, input regarding the textual content. The present invention may include modifying a playback of the media content based upon the input regarding the textual content to generate a modified media content. The present invention may include playing the modified media content.

Abstract: One or more memory systems, architectural structures, and/or methods of storing information in memory devices is disclosed to improve the data bandwidth and or to reduce the load on the communication links. The system may include one or more memory devices, one or more memory control circuits and one or more data buffer circuits. The memory system, architectural structure and/or method improves the ability of the communications links to transfer data downstream to the data buffer circuits. The memory control circuit receives a store command and a store data tag (Host tag) from a Host and sends the store data command and the store data tag to the data buffer circuits. No store data tag or control signal is sent over the communication links between the Host and the data buffer circuits, only data is sent over the communication links between the Host and the data buffer circuits.

Abstract: Distributing and rebalancing crewless vehicles (CV) by receiving a CV demand-availability gap, identifying candidate CV to relocate to close the CV demand-availability gap, identifying a candidate transport to relocate the CV to close the CV demand-availability gap, generating a candidate CV relocation plan, optimizing an overall CV relocation plan, and executing the overall CV relocation plan.

Abstract: An intent to return a product from a recipient that includes information about the product is received. An optimal social media influencer is selected from a plurality of social media influencers, based on the product information and the location of the product recipient, and information and location associated with respective influencers of the plurality of social media influencers. An agreement document is sent to the selected optimal social media influencer, and the agreement includes terms for the influencer to provide a posting of an activity associated with the product on a social media platform. In response to receiving a response accepting the agreement document, delivery information that includes a destination associated with the selected social media influencer is generated, and the delivery information associated with the selected optimal social media influencer is sent to the recipient of the product from which a shipping label can be printed.

Abstract: Embodiments are provided to simulate a quantum circuit. A system receives a quantum circuit (or its representation), generates a graph, and adds edges for each n-qubit of fusion to be applied. Costs are estimated or calculated for various paths of gate fusion between endpoints in the graph. One or more paths are selected, for example, the lowest cost path based on a Dijkstra algorithm evaluation. A unitary matrix for each gate fusion is then generated for simulating the quantum circuit. A simulation is performed locally or remotely based on the gate fusions along the selected one or more paths, and thus, improving the memory and processor performance of the simulation.

Abstract: Devices and/or computer-implemented methods facilitating static ZZ suppression and Purcell loss reduction using mode-selective coupling in two-junction superconducting qubits are provided. In an embodiment, a device can comprise a superconducting bus resonator. The device can further comprise a first superconducting qubit. The device can further comprise a second superconducting qubit, the first superconducting qubit and the second superconducting qubit respectively comprising: a first superconducting pad; a second superconducting pad; a third superconducting pad; a first Josephson Junction coupled to the first superconducting pad and the second superconducting pad; and a second Josephson Junction coupled to the second superconducting pad and the third superconducting pad. The first superconducting pad and the second superconducting pad of the first superconducting qubit and the second superconducting qubit are coupled to the superconducting bus resonator.

Abstract: A data structure is used to configure and transform a computer machine learning system. The data structure has one or more records where each record is a (vector) representation of a selected object in a corpus. One or more non-zero parameters in the records define the selected object and the number of the non-zero parameters define a word length of the record. One or more zero-value parameters are in one or more of the records. The word length of the object representation varies, e.g. can increase, as necessary to accurately represent the object within one or more contexts provided during training of a neural network used to create the database, e.g. as more and more contexts are introduced during the training. A minimum number of non-zero parameters are needed and zero-value parameters can be clustered together and compressed to save large amounts of system storage and shorten execution times.

Abstract: According to a first aspect of the present invention, a computer implemented method, a computer system and a computer program product for creating an ontological conversational agent, the method including creating an ontological specification of a domain of discourse of the ontological conversational agent, and creating a description of one or more goals of the ontological conversational agent. In an embodiment, the ontological description includes classes of entities, their associated attributes and relationships between the classes of entities. In an embodiment, the ontological description includes language-related descriptions. In an embodiment, the method, computer system and computer program product further includes creating a description of services of the ontological conversational agent.

Abstract: Techniques for candidate evaluation and filtering are provided. Enrollment criteria for a clinical trial are received, where the enrollment criteria include a plurality of conditions. A plurality of cost vectors is constructed for the plurality of conditions. A set of values for one or more of the plurality of conditions is determined for a candidate, where the set of values does not include a value for at least a first condition of the plurality of conditions. A utilized cost is generated for the candidate, based on the first set of values and the plurality of cost vectors. The candidate is then ranked based on the utilized cost.

Abstract: Embodiments relate to a system, program product, and method for smart contract implementation and management. A request for resources is modeled and a reservation of resources is captured in a first computation model interface. A provision of services is modeled as a second computation model interface. Compatibility of the first and second computation model interfaces is verified. Input and output actions are synchronized between the first and second computation model interfaces responsive to the compatibility verification. A smart contract is composed as a third computation model interface to model negotiation of contractual terms, including the captured resources with the provision of services. The composed smart contract is recorded in an operatively coupled immutable venue.

Abstract: Provided are techniques for mode-specific search query processing. A current search query is received from a user, wherein the user has a user profile. In response to determining that a query mode for the current search query is a guided mode, a query context of the current search query is determined. A classification for the current search query is determined. One or more search influencers are identified using the classification, where each of the one or more search influencers has a corresponding user profile. The current search query is rewritten based on the query context, a private portion of the user profile of the user, and a public portion of each corresponding user profile of each of the one or more search influencers. The rewritten search query is executed to generate search results, and the search results are returned.

Abstract: Embodiments relate to a system, program product, and method for proactively initiating throttle action on one or more cores in a multicore processing device to mitigate voltage droop therein. The method includes determining, in real-time, an indication of stall events within the core and determining one or more resolutions of the stall events. The method also includes determining, in real-time, a timing margin value for the core and predicting inducement of a voltage droop on the core. The method further includes integrating the resolutions of the stall events and the timing margin value for the core, determining, subject to the predicting, a throttle action for the core, and executing the throttle action on the core.

Abstract: Methods and systems for data center management include collecting sensor data from one or more sensors in a rack; determining a location and identifying information for each asset in the rack using a set of asset tags associated with respective assets; communicating the sensor and asset location to a communication module; receiving an instruction from the communication module; and executing the received instruction to change a property of the rack.

Abstract: A computer automatically groups IoT devices. The computer identifies at least one individual interaction zone within an operating environment, the at least one individual interaction zone characterized by pre-established device mounting regions each associated with a device category. The computer receives a location indicating signal from an IoT device located within the at least one individual interaction zone. The computer, in response to receiving the location indicating signal and based at least in part thereon, the computer recognizes that the IoT device is occupying one of the device mounting regions. In response to the recognition, the computer determines that the IoT device belongs to the device category associated with the occupied mounting region. In response to the determination, the computer adds the device to an IoT device group associated with the individual interaction zone associated with the occupied mounting region.

Abstract: A computer-implemented method includes identifying two or more memory locations and referencing, by a memory access request, the two or more memory locations. The memory access request is a single action pursuant to a memory protocol. The computer-implemented method further includes sending the memory access request from one or more processors to a node and fetching, by the node, data content from each of the two or more memory locations. The computer-implemented method further includes packaging, by the node, the data content from each of the two or more memory locations into a memory package, and returning the memory package from the node to the one or more processors. A corresponding computer program product and computer system are also disclosed.

Abstract: A method, system, and computer program product for automated testing of Internet of Things devices are provided. The method generates a device table for a set of devices. The device table includes a set of inputs and a set of controllable outputs for each device. A set of input signals are detected for a device for a subset of inputs associated with the device. The set of input signals are detected from one or more controllable outputs of a subset of devices. The device table is modified based on the set of input signals and the one or more controllable outputs to generate an association table representing a relation of the subset of inputs with the one or more controllable outputs. The method detects a fault in one or more device of the set of devices based on a test input signal and the association table.

Abstract: A set of workload criteria is determined from a workload associated with a plurality of sources. The workload is divided among a set of workload groups according to the set of workload criteria and a first workload scheduler. A set of edge computing resources is assigned to each workload group within the set according to the set of workload criteria and the set of workload groups. A portion of the workload associated with a subset of the plurality of sources is handled by a first subset of edge computing resources and a second workload scheduler, where the subset of sources is associated with a first workload group. The handling includes balancing, by the second workload scheduler, the portion of the workload among the subset of sources. The handled workload is reported to a control center.

Abstract: Core utilization optimization by dividing computational blocks across neurosynaptic cores is provided. In some embodiments, a neural network description describing a neural network is read. The neural network comprises a plurality of functional units on a plurality of cores. A functional unit is selected from the plurality of functional units. The functional unit is divided into a plurality of subunits. The plurality of subunits are connected to the neural network in place of the functional unit. The plurality of functional units and the plurality of subunits are reallocated between the plurality of cores. One or more unused cores are removed from the plurality of cores. An optimized neural network description is written based on the reallocation.

Abstract: A computer-implemented machine learning model training method and resulting machine learning model. One embodiment of the method may comprise receiving at a computer memory training data; and training on a computer processor a machine learning model on the received training data using a plurality of batch sizes to produce a trained processor. The training may include calculating a plurality of activations during a forward pass of the training and discarding at least some of the calculated plurality of activations after the forward pass of the training.