Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for identifying potential attacks through monitoring of user credential login attempts across a network of websites. One example method includes receiving a request associated with an authentication of a requestor at a landscape environment. In response to receiving the request encrypted credentials responsive to the request are loaded at a credential manager running at the landscape environment. The encrypted credentials are persisted at a storage by the credential manager. The encrypted credentials include credentials that are encrypted with a public key and are provided in encrypted form to the credential manager. The encrypted credentials are provided to a credential usage component running at the landscape environment for decrypting the encrypted credential with a private key persisted by the credential usage component at the landscape environment.

Abstract: One embodiment of the present invention sets forth a technique for generating augmented reality content. The technique includes inputting a first layout of a physical space and a first set of anchor content into a machine learning model. The technique also includes generating, via execution of the machine learning model, a first augmented reality view that includes (i) a first portion of the physical space and (ii) an extension of the first set of anchor content across a second portion of the physical space. The technique further includes causing the first augmented reality view to be outputted in a computing device.

Abstract: One embodiment of the present invention sets forth a technique for generating augmented reality (AR) content. The technique includes inputting a first layout of a physical space and a first set of anchor content into a machine learning model. The technique also includes generating, via execution of the machine learning model, a first three-dimensional (3D) volume that includes (i) a first subset of the physical space and (ii) a placement of one or more 3D representations of the first set of anchor content in a second subset of the physical space. The technique further includes causing one or more views of the first 3D volume to be outputted in a computing device.

Abstract: Techniques for automatically placing and manipulating virtual objects of augmented reality (AR) and mixed reality (MR) simulations are described. One technique includes obtaining an indication of at least one virtual object available for placing within a real-world environment during an AR simulation or a MR simulation. A first representation of the real-world environment is generated, based on a scan of the real-world environment. At least one second representation of the real-world environment is generated from the first representation. A match is determined between the at least one virtual object and at least one available space within the real-world environment, based at least in part on evaluating the at least one virtual object and the at least one second representation with a machine learning model(s). The at least one virtual object is rendered on a computing device, based on the match.

Abstract: Programs, systems, and methods are described for providing on-demand applications to users by infrastructure as a service platforms. A request catcher may receive a request to start an application that runs on a managed system that is scheduled to be off. The request catcher or a management application may verify that the user has authorization to access the application and request to the management application to start the managed system. The management application may start the managed system and the managed system may then provide access to the application to the user.

Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.

Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.

Abstract: A system includes a computing platform having a hardware processor, and a system memory storing a software code and a content labeling predictive model. The hardware processor is configured to execute the software code to scan a database to identify content assets stored in the database, parse metadata stored in the database to identify labels associated with the content assets, and generate a graph by creating multiple first links linking each of the content assets to its corresponding label or labels. The hardware processor is configured to further execute the software code to train, using the graph, the content labeling predictive model, to identify, using the trained content labeling predictive model, multiple second links among the content assets and the labels, and to annotate the content assets based on the second links.

Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.

Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.

Abstract: The invention relates to a device for removing at least one cooling element from an at least partially demoulded cast part, in particular from a cast housing for an electric motor formed from a light metal alloy, which has an apparatus for removing the at least one cooling element. Furthermore, the invention relates to a method for removing at least one cooling element from an at least partially demoulded cast part, a method for introducing at least one cooling element into a mould core of a cast part mould, a cooling element, and a cast part.

Abstract: A system includes a computing platform having a hardware processor, and a system memory storing a software code and a content labeling predictive model. The hardware processor is configured to execute the software code to scan a database to identify content assets stored in the database, parse metadata stored in the database to identify labels associated with the content assets, and generate a graph by creating multiple first links linking each of the content assets to its corresponding label or labels. The hardware processor is configured to further execute the software code to train, using the graph, the content labeling predictive model, to identify, using the trained content labeling predictive model, multiple second links among the content assets and the labels, and to annotate the content assets based on the second links.

Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.

Abstract: Methods, systems, and computer-readable storage media for executing service calls, and include receiving a service call including a flat structure of a set of key-value pairs, providing a blueprint including one or more blueprint expressions, for at least one blueprint expression included in the blueprint, determining a specific value by processing at least one blueprint expression of the blueprint using a value of a key-value pair, and providing the specific value for execution of the process.

Abstract: Methods, systems, and computer-readable storage media for executing service calls, and include receiving a service call including a flat structure of a set of key-value pairs, providing a blueprint including one or more blueprint expressions, for at least one blueprint expression included in the blueprint, determining a specific value by processing at least one blueprint expression of the blueprint using a value of a key-value pair, and providing the specific value for execution of the process.

Abstract: Methods, systems, and computer-readable storage media for receiving a request to execute a custom process over a target system, the custom process including one or more operations, determining, for each operation of the custom process, a set of entities implicated by a respective operation based on an operations graph of the custom process, and relations paths of the respective operation, determining, for each set of entities, a sub-set of entities based on one or more constraints of the respective operation, providing, for each operation of the custom process, an operation process that is to be executed over one or more entities of a respective sub-set of entities, combining operation processes into a single custom process, and initiating execution of the single custom process.

Abstract: Techniques are described for automatically analyzing received values to determine their semantic meaning and apply one or more formatting modifications and/or emphases to the received values based on the determined semantic meaning. In one example, a value to be formatted based on a semantic context associated with at least two portions of the received value is received. In response, a semantic rules associated with the received value is identified. The received value is semantically processed using the semantic rules, where processing includes identifying at least two portions of the value corresponding to their contexts. At least one formatting rule is determined as associated with the two or more semantic contexts, each formatting rule associated with a particular context. The formatting rules are applied to the corresponding portions of the received values associated their semantic contexts to generate a modified version of the received value, which is then provided for presentation.

Abstract: Methods, systems, and computer-readable storage media for receiving a request to execute a custom process over a target system, the custom process including one or more operations, determining, for each operation of the custom process, a set of entities implicated by a respective operation based on an operations graph of the custom process, and relations paths of the respective operation, determining, for each set of entities, a sub-set of entities based on one or more constraints of the respective operation, providing, for each operation of the custom process, an operation process that is to be executed over one or more entities of a respective sub-set of entities, combining operation processes into a single custom process, and initiating execution of the single custom process.

Abstract: Protein variants of CTLA-4, which show increased activity compared to wild-type in a cell assay and which exhibit increased stability. Such variants are useful for treatment of disorders whereby attenuation of the T cell response would be beneficial.