Abstract: A method, computer system, and a computer program product for integrating uncertainty quantifications into a long-term risk based calendar is provided. The present invention may include receiving data for an organization. The present invention may include generating a long-term risk based calendar using the data received for the organization. The present invention may include presenting the long-term risk based calendar to a plurality of user associated with the organization. The present invention may include providing one or more recommendations to at least one of the plurality of users.

Abstract: Initial and boundary conditions, and parameters associated with geophysical modeling can be received. Based on the received initial and boundary conditions and parameters, a multiscale model can be trained for data generation to produce first resolution simulation data and second resolution simulation data for a surrogate machine learning model training, where the second resolution simulation data has higher resolution than the first resolution simulation data. A surrogate model can be created using neural operators, where the surrogate model is trained using the first resolution simulation data and second resolution simulation data. An operational forecasting model can be generated using the surrogate model.

Abstract: A modeling problem can be received. A database of prior calibrated models can be searched to identify a similar problem having features similar to the received modeling problem. The modeling problem can be calibrated using information of the identified similar problem. The accuracy of calibrated modeling problem can be monitored. The modeling problem can be recalibrated until a performance criterion is met. Calibrated modeling problem can be stored in the database.

Abstract: Physical assistance for identification of an input location on a touchscreen may include detecting that a user has contacted a touchscreen at an initial touch point with a pointing device, predicting an input location from one or more input locations for receiving input on the touchscreen, determining a path from the initial touch point to the predicted input location, and generating a physical cursor on the touchscreen at a location proximate to the pointing device, wherein the physical cursor is a raised portion of the touchscreen.

Abstract: In an embodiment, a method of sketching using a hybrid quantum-classical system includes creating a set of clustered data sets from a first data set. In an embodiment, the method includes evaluating, using a quantum processor and quantum memory, the set of clustered data sets. In an embodiment, the method includes evaluating, using the quantum processor and quantum memory, a set of quality metrics for the set of clustered data sets. In an embodiment, the method includes reclustering, responsive to at least one of the set of quality metrics failing to meet a quality criterion, the first data set.

Abstract: Techniques for providing a reusable filled bottle are provided. A stations receives a bottle request from a user and uses information from the request to select and associate a sanitized bottle to the user. The user is charged for a bottle rental and provided with a filled reusable bottle. The user may also return the bottle to the station to have the bottle refilled or return the bottle such that the bottle rental is completed and the bottle no longer associated with the user.

Abstract: Physical assistance for identification of an input location on a touchscreen may include detecting that a user has contacted a touchscreen at an initial touch point with a pointing device, predicting an input location from one or more input locations for receiving input on the touchscreen, determining a path from the initial touch point to the predicted input location, and generating a physical cursor on the touchscreen at a location proximate to the pointing device, wherein the physical cursor is a raised portion of the touchscreen.

Abstract: Techniques for providing a reusable filled bottle are provided. A stations receives a bottle request from a user and uses information from the request to select and associate a sanitized bottle to the user. The user is charged for a bottle rental and provided with a filled reusable bottle. The user may also return the bottle to the station to have the bottle refilled or return the bottle such that the bottle rental is completed and the bottle no longer associated with the user.

Abstract: Techniques for generating shopping routes through shopping environments are provided. A plurality of identified shopping list items are used to generate a shopping list for a user where the shopping list items are based on items the user needs. The shopping list is updated using a processed inventory image which identifies inventory items located in an inventory of the user. Items the user currently has are reduced or removed from the shopping list. When the shopping list is complete, a shopping route through a shopping environment is generated for the shopping list using a crowd-sourced or community updated map to provide an efficient route to the shopping list items in the shopping environment.

Abstract: A notification message is received by a communication device The notification message includes a message identifier and a digital signature generated by an original sender of the notification message. That an integrity of message content in the notification message and an original sender of the notification message is verified is determined using the digital signature. Responsive to determining that the notification message is verified, the notification message is forwarded by the communication device using a peer-to-peer communication channel over a mesh network.

Abstract: Hybrid classical-quantum decision maker training includes receiving a training data set, and selecting, by a first processor, a sampling of objects from the training set, each object represented by at least one vector. A quantum processor applies a quantum feature map to the selected objects to produce one or more output vectors. The first processor determines one or more distance measures between pairs of the output vectors, and determines at least one portion of the quantum feature map to modify the classical feature map. The first processor adds an implementation of the at least one portion of the quantum feature map to the classical feature map to generate an updated classical feature map.

Abstract: A method, computer system, and computer program product for a cognitive, real-time feedback speaking coach are provided. The embodiment may include capturing a plurality of text from a prepared document. The embodiment may also include capturing a plurality of user voice data and a plurality of user movement data. The embodiment may further include calculating a speaker rating based on the plurality of received user voice data, the plurality of received user movement data, and the plurality of captured text. The embodiment may also include identifying one or more points of improvement based on the calculated speaker rating. The embodiment may further include alerting a user of the one or more identified points of improvement.

Abstract: A distributed file system (DFS) is provided that is configured to store data in a General Parallel File system (GPFS) and interface with a client configured to interface with a HADOOP Distributed File System (HDFS). The DFS includes a first Node; and a plurality of second Nodes including the GPFS. The first Node is configured to convert an HDFS command from the client into a GPFS command, apply the GPFS command to the GPFS to access a GPFS file, format an HDFS data structure to include identifiers of a set of the second nodes storing the GPFS file, a filename of the GPFS file, and an offset into the GFPS file, and send the HDFS data structure to the client. Each of the second Nodes is configured to access the GPFS using a part of the HDFS data structure received from the client.

Abstract: A first feature set for an accident is identified, based on one or more historical accident reports. The accident is geolocated to obtain road data for a location of the accident. Weather data for the accident is obtained, based on the accident location. Hidden data for the accident is obtained, based on the accident location and the time of the accident. The first feature set is combined with the road data, weather data, and hidden data to produce a complete feature set for the accident. A model feature set is generated, based on a combination of the complete feature set and a plurality of other complete feature sets produced based on historical accident reports for a plurality of other accidents.

Abstract: A method and system of storing and retrieving data in a computer system are disclosed. In an embodiment, the method comprises storing a data object at a specified location in a first data store, storing in a second data store first information identifying said specified location, retrieving the data object from a defined location in the first data store, obtaining second information identifying the defined location in the first data store, and comparing the second information to the first information to determine if the first information and the second information identify a same location in the first data store. In embodiments of the invention, the method further comprises when the second information and the first information identify different locations in the first data store, generating a fault signal to indicate that the location of the data object in the first data store has changed.

Abstract: A method, computer system, and a computer program product for identifying feature importance in deep learning models is provided. Embodiments of the present invention may include building a reconstruction model. Embodiments of the present invention may include intercepting an output of a trained prediction model at a bottleneck layer. Embodiments of the present invention may include processing the output of the trained model using the reconstruction model. Embodiments of the present invention may include identifying a plurality of features based on the reconstruction model.

Abstract: In an embodiment, a method of sketching using a hybrid quantum-classical system includes creating a set of clustered data sets from a first data set. In an embodiment, the method includes evaluating, using a quantum processor and quantum memory, the set of clustered data sets. In an embodiment, the method includes evaluating, using the quantum processor and quantum memory, a set of quality metrics for the set of clustered data sets. In an embodiment, the method includes reclustering, responsive to at least one of the set of quality metrics failing to meet a quality criterion, the first data set.

Abstract: In an embodiment, a method includes classifying, using a neural network including quantum components, a data set to generate a first set of classified data. In the embodiment, the method includes generating noise in the quantum components. In the embodiment, the method includes reclassifying, using the neural network, the data set with the generated noise to generate a second set of classified data. In the embodiment, the method includes determining, responsive to comparing the first set of classified data and the second set of classified data, a sensitivity of the quantum components.

Abstract: Hybrid classical-quantum decision maker training includes receiving a training data set, and selecting, by a first processor, a sampling of objects from the training set, each object represented by at least one vector. A quantum processor applies a quantum feature map to the selected objects to produce one or more output vectors. The first processor determines one or more distance measures between pairs of the output vectors, and determines at least one portion of the quantum feature map to modify the classical feature map. The first processor adds an implementation of the at least one portion of the quantum feature map to the classical feature map to generate an updated classical feature map.

Abstract: Dynamic graph adaptation for stream processing over hybrid, physically disparate analytics platforms, by means of a computer-implemented method that includes obtaining a streaming application graph, generating a partitioned graph by partitioning the streaming application graph in response to a topology descriptor and a partitioning algorithm, compiling the partitioned graph into a plurality of subgraphs for deployment to a plurality of respective runtimes that are described by the topology descriptor, and deploying the plurality of subgraphs to the plurality of respective runtimes.