Abstract: A method includes distributing a number of printer devices across a computer network with corresponding logistical nodes, and, through a server, dynamically extracting, from a number of digital pre-print files, metadata related to documents and pre-conditions based on scanning thereof, and classifying the number of digital pre-print files into groups based on an extent to which the dynamically extracted metadata is shared between constituents thereof.

Abstract: Provided is a system and method for writing IoT data of an object in transit and business context associated with the IoT data and/or the object to a blockchain. In one example, the method may include receiving sensor data of an object that is in a multi-party transactional process, identifying dynamic context of the object based on a current position of the object within the multi-party transactional process, generating a data block comprising the received sensor data of the object and the identified dynamic context of the object, and storing the data block within a blockchain on an immutable distributed ledger.

Abstract: A computer-implemented method, a computer program product, and a computer system for efficient use of computing resources in two stage training of a deep learning model. A computer executes a first first-stage training job to train a deep learning model. The computer finishes the first first-stage training job by using early stopping and then registers a first second-stage training job to train a deep learning model that has been trained in the first first-stage training job. The computer executes the first second-stage training job with a small number of epochs. The computer interrupts the first second-stage training job and executes a second first-stage training job, in response to receiving a registration of the second first-stage training job. The computer interrupts the first second-stage training job and executes a second second-stage training job that has a higher priority, in response to receiving a registration of the second second-stage training job.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for self-optimization of computing environments. In some implementations, different combinations of settings used by one or more server environments are determined. Results achieved by the one or more server environments are monitored when using the different combinations of settings. Based on the monitored results, one or more performance measures are generated that correspond to each of the different combinations of settings. An updated set of settings are selected for a particular server environment based on the performance measures. The selected settings are provided for the particular server environment.

Abstract: A method for generating an executable program to run on a system of one or more processor chips each comprising a plurality of tiles. The method comprises: receiving a graph comprising a plurality of data nodes, compute vertices and directional edges, wherein the graph is received in a first graph format that does not specify which data nodes and vertices are allocated to which of the tiles; and generating an application programming interface, API, for converting the graph, to determine a tile-mapping allocating the data nodes and vertices amongst the tiles. The generating of the API comprises searching the graph to identify compute vertices which match any of a predetermined set of one or more compute vertex types. The API is then called to convert the graph to a second graph format that includes the tile-mapping, including the allocation by the assigned memory allocation functions.

Abstract: A method for distributing an application for an edge computing device performed by a computing device according to an embodiment of the present invention includes selecting a cluster including two or more edge devices from among a plurality of edge devices, distributing a first application to a second edge device included in the cluster, modifying routing information such that a service request incoming to a first edge device included in the cluster is transmitted to the second edge device, and replacing the first application running in the first edge device with a second application.

Abstract: A method for generating an executable program to run on a system of one or more processor chips each comprising a plurality of tiles. The method comprises: receiving a graph comprising a plurality of data nodes, compute vertices and directional edges, wherein the graph is received in a first graph format that does not specify which data nodes and vertices are allocated to which of the tiles; and generating an application programming interface, API, for converting the graph, to determine a tile-mapping allocating the data nodes and vertices amongst the tiles. The generating of the API comprises searching the graph to identify compute vertices which match any of a predetermined set of one or more compute vertex types. The API is then called to convert the graph to a second graph format that includes the tile-mapping, including the allocation by the assigned memory allocation functions.

Abstract: Some examples described herein relate to packet identification (ID) assignment for a routing network in a programmable integrated circuit (IC). In an example, a design system includes a processor and a memory coupled to the processor. The memory stores instruction code. The processor is configured to execute the instruction code to construct an interference graph based on routes of logical nets through switches in a routing network, and assign identifications to the routes comprising performing vertex coloring of vertices of the interference graph. The interference graph includes the vertices and interference edges. Each vertex represents one of the logical nets having a route. Each interference edge connects two vertices that represent corresponding two logical nets that have routes that share at least one port of a switch. The identifications correspond to values assigned to the vertices by the vertex coloring.

Abstract: Systems, methods, and devices, including computer-readable media, for managing operation of devices in complex systems and changing environments. In some implementations, a server system stores data indicating management plans for each of a plurality of different devices, each management plan indicating a device-specific set of program states for programs in a predetermined set of programs. The server system alters the management plans and enforces interdependence of the programs, and the server system generates a customized instruction that alters operation of the device according to the device-specific set of program states assigned in the altered management plan for the device. The server system causes each device to perform one or more operations of the device determined according to the device-specific set of program states assigned in the altered management plan for the device.

Abstract: Described herein are systems, apparatus, methods and computer program products configured for actions to be performed on segments within a pipeline. The disclosed systems and techniques allow a user to select a data segment on a graphical user interface. The user may then utilize the data of the segment to perform one or more actions. Thus the data of the segment may be used to provide communications to parties contained within the data, manipulate the data segment based on the parties contained within the data, and change the data displayed based on attributes selected.

Abstract: A system for controlling access to a downstream database management system (DMS) is provided. The system comprises an interface to maintain client connections with a plurality of upstream clients; a pooling component to establish a dynamic pool, and to selectively route each client connection to a pool; within each pool maintaining a queue comprising client database requests associated with particular client connections; and selectively granting access to the client database requests within each queue to at least one downstream DMS.

Abstract: Techniques for phased deployment of machine learning models are described. Customers can call a training API to initiate model training, but then must wait while the training completes before the model can be used to perform inference. Depending on the type of model, machine learning algorithm being used for training, size of the training dataset, etc. this training process may take hours or days to complete. This leads to significant downtime where inference requests cannot be served. Embodiments improve upon existing systems by providing phased deployment of custom models. For example, a simple, less accurate model, can be provided synchronously in response to a request for a custom model. At the same time, one or more machine learning models can be trained asynchronously in the background. When the machine learning model is ready for use, the customers' traffic and jobs can be transferred over to the better model.

Abstract: Techniques for heterogeneous compute instance auto-scaling with reinforcement learning (RL) are described. A user specifies a reward function that generates rewards for use with an application simulation for determining what different instance types should be added to or removed from the application as part of training a RL model. The RL model can be automatically deployed and used to monitor an application to automatically scale the application fleet using heterogenous compute instances.

Abstract: Method and apparatus for performing feature engineering using negative inferences are provided. One example method generally includes identifying a plurality of concepts and analyzing a corpus of documents to determine a first co-occurrence rate for a first concept and a second concept in the plurality of concepts. The method further includes analyzing the corpus of documents to determine a second co-occurrence rate for the second concept and at least a third concept of a set of concepts related to the first concept and determining an inverse relationship between the second concept and the third concept. The method further includes generating test data for training a machine learning model including a negative inference between the second concept and the third concept and training the machine learning model using the test data.

Abstract: Example implementations relate to predicted usage based on monitored usage. For example, a system comprising a monitor engine can monitor usage of a plurality of applications used by a user during a first time period, during a heartbeat event, and predict usage of the plurality of applications, using a predictor engine, by the user during a second time period based on the analyzed monitored usage of the plurality of applications during the first time period. Additionally, the predictor engine can generate content during the second time period based on the predicated usage of the plurality of applications during the first time period.

Abstract: The present disclosure involves systems, software, and computer implemented methods for defining and using a system metamodel for an event-driven cluster of microservices. A deployment package that includes metadata definitions is received for a microservice. The metadata definitions include: a reference to a service; a reference to a resource entity served by the service; a reference to an event subscribed to by the service; a reference to a micro frontend that provides a user interface for the service; and a reference to an API for performing operations on the resource entity. The microservice is deployed, which includes including the metadata definitions in a system metamodel graph. The microservice is activated, including generating at least one event corresponding to including the metadata definitions in the system metamodel graph. In response to a request for metadata for the microservice, the system metamodel graph is traversed to retrieve the requested metadata.

Abstract: The invention generally relates to managing and sharing augmented reality (AR) content, and, more specifically, to an AR platform providing synchronized sharing of AR content in real time across multiple AR devices.

Abstract: A number of variations may include providing a computer system for networked video management, the computer system may include at least one computer having non-transitory memory for storing machine instructions that are to be executed by the computer, the machine instructions when executed by the computer implement the following web service proxy protocol: opening at least one first network connection from at least one video gateway device to at least one web service proxy application and opening at least one second network connection from the at least one video gateway device to at least one network camera on a server network; performing a scan for network cameras on the server network via the at least one gateway device and transmitting the results of the scan to the web service proxy application; and establishing at least one tunnel between the at least one network camera and the web service proxy application.

Abstract: In some implementations, a system enables an administrator to customize a set of rules to dynamically adjust the configuration and output of an application provided to users. A configuration interface for setting rules that dynamically adjust output of an application is provided. Data indicating one or more rules are received through the configuration interface. Activity data indicating user interaction with the application or sensor data for at least some of a plurality of users of the application are then received from multiple client devices. A determination relating to the activity data satisfying at least one condition or trigger is then made. Instructions to adjust output of the application according to one or more system actions of the one or more rules are then communicated to client devices associated with the users in the first subset of the plurality of users.

Abstract: Disclosed are some examples of systems, methods, and computer program products for suggesting actions for evaluating user performance in a social networking system. In some implementations, textual input of a social media message is processed. The processing can include determining that data of the textual input indicates an evaluation of performance of a user of the social networking system. A prompt can be displayed, requesting submission of information evaluating performance of the user. A performance evaluation record can be generated or updated in a database to store the evaluating information in association with an identification of the user, and an indication of the evaluating information can be shared in a feed of the social networking system.