Abstract: The present invention is related to a class of stable 2D covalent organic frameworks with multiple dimethyl amino groups that can trap carbon dioxide at ambient temperature and pressure, and an economical, environmentally-friendly process for the generation of transient surface charges and subsequent self-exfoliation of the COF into ultrathin nanosheets. The said exfoliated material possess activity against pathogenic bacteria. The invention further discloses a carbon dioxide induced exfoliation process that is completely reversible upon heat treatment, whereby control over bacterial growth is achieved via an efficient antibiotic switch.

Abstract: A method for improving computing efficiency of a computing device for temporal directed cycle detection in a transaction graph includes preparing the transaction graph based on a plurality of transactions, the transaction graph including nodes indicating transaction origination points and transaction destination points, and edges indicating interactions between the nodes. Irrelevant nodes in the transaction graph are identified and pruned to provide a pruned, preprocessed transaction graph which can be partitioning into sections, where each section includes selected nodes that are linked to other linked nodes therein. Each of the sections having non-cyclic nodes can be trimmed prior to performing cycle detection on the resulting pruned transaction graph. Postprocessing pruning can be performed to further reduce the number of detected cycles that may be of interest to a particular application, such as in anti-money laundering.

Abstract: A method, computer program product, and computer system for performing an Application Programming Interface (API) service using zone-based topics within a publish/subscribe (pub/sub) messaging infrastructure. An API service request sent by a client entity is received and specifies an API service to be fulfilled. A selection of an API service endpoint configured to execute the API service is received. Messages are posted to respective pub/sub zone-based topics, resulting in selection of workers subscribed to the respective zone-based topics. Each zone-based topic includes tasks to be performed in a specified one or more zones. For each zone-based topic, the tasks of the zone-based topic are implemented by executing the worker selected for the zone-based topic. The tasks of the zone-based topics include invoking the API service endpoint for the requested API service and making a fulfillment result of the API service available to the client entity.

Abstract: A processor training a reinforcement learning model can include receiving a first dataset representing an observable state in reinforcement learning to train a machine to perform an action. The processor receives a second dataset. Using the second dataset, the processor trains a machine learning classifier to make a prediction about an entity related to the action. The processor extracts an embedding from the trained machine learning classifier, and augments the observable state with the embedding to create an augmented state. Based on the augmented state, the processor trains a reinforcement learning model to learn a policy for performing the action, the policy including a mapping from state space to action space.

Abstract: A method, computer system, and a computer program product for energy efficient data exchange is provided. The present invention may include a first electronic card device (ECD) including a first switch and a second switch. The present invention may include the first switch being configured to power on the first ECD responsive to the first ECD engaging a second ECD. The present invention may include the first ECD being configured to exchange data with the second ECD. The present invention may include a docking component configured to receive the first ECD. The present invention may include the docking component including an actuator configured to engage the second switch to power on the first ECD when the first ECD is received by the docking component. The present invention may include the first ECD configured to transfer received data from the second ECD to a mobile device associated with the docking component.

Abstract: Methods and systems for explainable machine learning are described. In an example, a processor can receive a data set from a plurality of data sources corresponding to a plurality of domains. The processor can train a machine learning model to learn a plurality of vectors that indicate impact of the plurality of domains on a plurality of assets. The machine learning model can be operable to generate forecasts relating to performance metrics of the plurality of assets based on the plurality of vectors. In some examples, the machine learning model can be a neural attention network with shared hidden layers.

Abstract: A task-based learning using task-directed prediction network can be provided. Training data can be received. Contextual information associated with a task-based criterion can be received. A machine learning model can be trained using the training data. A loss function computed during training of the machine learning model integrates the task-based criterion, and minimizing the loss function during training iterations includes minimizing the task-based criterion.

Abstract: A graph neural network can be built and trained to predict a risk of an entity. A multi-relational graph network can include a first graph network and a second graph network. The first graph network can include a first set of nodes and a first set of edges connecting some of the nodes in the first set. The second graph network can include a second set of nodes and a second set of edges connecting some of the nodes in the second set. The first set of nodes and the second set of nodes can represent entities, the first set of edges can represent a first relationship between the entities and the second set of edges can represent a second relationship between the entities. A graph convolutional network (GCN) can be structured to incorporate the multi-relational graph network, and trained to predict a risk associated with a given entity.

Abstract: A deep-learning neural network can be trained to model a probability distribution of the asset-price trends for a future time period using a training data set, which can include asset-price trends of a plurality of assets over a past time period and a latent vector sampled from a prior distribution associated with the asset-price trends of a plurality of assets. The training data set can represent a time series data. A portfolio optimization can be executed on the modeled probability distribution to estimate expected risks and returns for different portfolio diversification options.

Abstract: A processor training a reinforcement learning model can include receiving a first dataset representing an observable state in reinforcement learning to train a machine to perform an action. The processor receives a second dataset. Using the second dataset, the processor trains a machine learning classifier to make a prediction about an entity related to the action. The processor extracts an embedding from the trained machine learning classifier, and augments the observable state with the embedding to create an augmented state. Based on the augmented state, the processor trains a reinforcement learning model to learn a policy for performing the action, the policy including a mapping from state space to action space.

Abstract: A task-based learning using task-directed prediction network can be provided. Training data can be received. Contextual information associated with a task-based criterion can be received. A machine learning model can be trained using the training data. A loss function computed during training of the machine learning model integrates the task-based criterion, and minimizing the loss function during training iterations includes minimizing the task-based criterion.

Abstract: A method, computer system, and a computer program product for energy efficient data exchange is provided. The present invention may include a first electronic card device (ECD) including a first switch and a second switch. The present invention may include the first switch being configured to power on the first ECD responsive to the first ECD engaging a second ECD. The present invention may include the first ECD being configured to exchange data with the second ECD. The present invention may include a docking component configured to receive the first ECD. The present invention may include the docking component including an actuator configured to engage the second switch to power on the first ECD when the first ECD is received by the docking component. The present invention may include the first ECD configured to transfer received data from the second ECD to a mobile device associated with the docking component.

Abstract: A method for improving computing efficiency of a computing device for temporal directed cycle detection in a transaction graph includes preparing the transaction graph based on a plurality of transactions, the transaction graph including nodes indicating transaction origination points and transaction destination points, and edges indicating interactions between the nodes. Irrelevant nodes in the transaction graph are identified and pruned to provide a pruned, preprocessed transaction graph which can be partitioning into sections, where each section includes selected nodes that are linked to other linked nodes therein. Each of the sections having non-cyclic nodes can be trimmed prior to performing cycle detection on the resulting pruned transaction graph. Postprocessing pruning can be performed to further reduce the number of detected cycles that may be of interest to a particular application, such as in anti-money laundering.

Abstract: A deep-learning neural network can be trained to model a probability distribution of the asset-price trends for a future time period using a training data set, which can include asset-price trends of a plurality of assets over a past time period and a latent vector sampled from a prior distribution associated with the asset-price trends of a plurality of assets. The training data set can represent a time series data. A portfolio optimization can be executed on the modeled probability distribution to estimate expected risks and returns for different portfolio diversification options.

Abstract: Methods and systems for explainable machine learning are described. In an example, a processor can receive a data set from a plurality of data sources corresponding to a plurality of domains. The processor can train a machine learning model to learn a plurality of vectors that indicate impact of the plurality of domains on a plurality of assets. The machine learning model can be operable to generate forecasts relating to performance metrics of the plurality of assets based on the plurality of vectors. In some examples, the machine learning model can be a neural attention network with shared hidden layers.

Abstract: Access is obtained to a plurality of intermediately transformed electronic documents (with a plurality of sections and subsections) which have been transformed, by topical analysis and text summarization techniques, from a plurality of original electronic documents comprising at least some unstructured electronic documents. Audit and retrieval agent code is appended to the sections and subsections to create a plurality of finally transformed electronic documents. Users are allowed to access the finally transformed electronic documents. The users are provided with accountability reminders contemporaneous with the access. The access of the users to the sections and subsections of the finally transformed electronic documents is logged. An audit report is provided based on the logging. Also provided is a cloud service for enterprise-level sensitive data protection with variable data granularity, using one or more one guest virtual machine images.

Abstract: An example operation may include one or more of receiving, by a blockchain node, a request to transfer an asset, generating a blockchain transaction, obtaining one or more rules from a smart contract corresponding to a smart contract identifier, and comparing one or more parameters to the one or more rules to obtain a risk level. In response to the risk level being greater than a threshold, the example operation includes not executing the blockchain transaction. In response to the risk level not being greater than the threshold, the example operation includes executing the transaction. The request includes the smart contract identifier and the one or more parameters. The asset includes one of a trade item or a service to be performed. The blockchain transaction includes the smart contract identifier and the one or more parameters.

Abstract: Access is obtained to a plurality of intermediately transformed electronic documents (with a plurality of sections and subsections) which have been transformed, by topical analysis and text summarization techniques, from a plurality of original electronic documents comprising at least some unstructured electronic documents. Audit and retrieval agent code is appended to the sections and subsections to create a plurality of finally transformed electronic documents. Users are allowed to access the finally transformed electronic documents. The users are provided with accountability reminders contemporaneous with the access. The access of the users to the sections and subsections of the finally transformed electronic documents is logged. An audit report is provided based on the logging. Also provided is a cloud service for enterprise-level sensitive data protection with variable data granularity, using one or more one guest virtual machine images.

Abstract: Access is obtained to a plurality of intermediately transformed electronic documents (with a plurality of sections and subsections) which have been transformed, by topical analysis and text summarization techniques, from a plurality of original electronic documents comprising at least some unstructured electronic documents. Audit and retrieval agent code is appended to the sections and subsections to create a plurality of finally transformed electronic documents. Users are allowed to access the finally transformed electronic documents. The users are provided with accountability reminders contemporaneous with the access. The access of the users to the sections and subsections of the finally transformed electronic documents is logged. An audit report is provided based on the logging. Also provided is a cloud service for enterprise-level sensitive data protection with variable data granularity, using one or more one guest virtual machine images.

Abstract: A method, system and computer program product for autonomic security configuration may include controlling a security configuration of at least one resource forming a solution based on a plurality of security requirements. The method may further include applying the plurality of security requirements across a plurality of resources independent of a resource type.