Abstract: The technology described herein is directed to spatial interface for multi-modal input to artificial intelligence (AI) powered tools. The interface allows for a first mode of input, such as selection of one or more objects using a movable window that can be resized and reshaped by a user. In addition, the interface allows for a second mode of input, such as text or voice commands. The AI powered tools accept the inputs from the first and second modes and dynamically generates a response.

Abstract: The presently-disclosed subject matter relates to crosslinkers, compositions, and methods for trapping a target of interest on a substrate of interest. The methods may be used to inhibit and treat pathogen infection and provide contraception. The methods may be used to trap or separate particles and other substances. The subject matter further relates to methods of identifying and preparing optimal crosslinkers and methods for manipulating targets of interest.

Abstract: The presently-disclosed subject matter relates to crosslinkers, compositions, and methods for trapping a target of interest on a substrate of interest. The methods may be used to inhibit and treat pathogen infection and provide contraception. The methods may be used to trap or separate particles and other substances. The subject matter further relates to methods of identifying and preparing optimal crosslinkers and methods for manipulating targets of interest.

Abstract: According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a part parameter dictionary module comprising a processor, geometry data for a plurality of geometric structures forming a plurality of parts, wherein the parts are manufactured with an additive manufacturing machine; determining, using the processor of the part parameter dictionary module, a feature set for each geometric structure; generating, using the processor of the part parameter dictionary module, one of a coupon and a coupon set for the feature set; generating an optimized parameter set for each coupon, using the processor of the part parameter dictionary module, via execution of an iterative learning control process for each coupon; mapping, using the processor of the part parameter dictionary module, one or more parameters of the optimized parameter set to one or more features of the feature set; and generating a dictionary of optimized scan parameter sets to fabricate geometric str

Abstract: One or more processors may output for display, an interface including a data classification section including two or more class nodes, a training section including a training node, and an evaluation section including an evaluation node. At a first class node a first set of training data may be captured and at a second class node a second set of training data may be captured. In response to an input received at the training node, a classification model based on the first set of training data and the second set of training data may be trained. Evaluation data may be captured in an evaluation node, and using the trained classification model, classifications for each piece of the evaluation data may be determined. A visual representation of the classification for each piece of the evaluation data may be output for display within the evaluation node.

Abstract: The presently-disclosed subject matter relates to crosslinkers, compositions, and methods for trapping a target of interest on a substrate of interest. The methods may be used to inhibit and treat pathogen infection and provide contraception. The methods may be used to trap or separate particles and other substances. The subject matter further relates to methods of identifying and preparing optimal crosslinkers and methods for manipulating targets of interest.

Abstract: Provided herein are data cloud administration techniques that achieve autonomy by using a rules engine that reacts to a database system event by autonomously submitting an asynchronous job to reconfigure a database. In an embodiment, a rules engine receives an event from a DBMS. Based on the event, the rules engine executes a rule to generate a request that indicates configuration details for a database. The rules engine sends the request to a request broker. The request broker dispatches an asynchronous job based on the request. The asynchronous job configures the database based on the configuration details. Thus, databases in a cloud, data grid, or data center may be administered autonomously (without human intervention) base on dynamic conditions that are foreseen and unforeseen.

Abstract: Systems and methods disclosed herein include systems and methods for delivering secondary content to a plurality of user devices, the secondary content comprising events synchronized to primary content. The process can include: delivering an application to a plurality of user devices, the application configured to play or executed secondary content on the user devices; and causing the secondary content executed on the user devices to be synchronized with primary content.

Abstract: Provided herein are workload management techniques that asynchronously configure pluggable databases within a compute cloud. In an embodiment, the compute cloud receives an administrative request that indicates configuration details for a pluggable database. The compute cloud generates a configuration descriptor that specifies an asynchronous job based on the configuration details of the request. The compute cloud accesses hosting metadata to detect at least one of: a) a current container database that already hosts the pluggable database, b) a target container database that will host the pluggable database, or c) a particular computer that hosts at least one of: the current container database, or the target container database. The compute cloud executes the asynchronous job to configure the pluggable database based on at least one of: the hosting metadata, or the configuration descriptor.

Abstract: One or more processors may output for display, an interface including a data classification section including two or more class nodes, a training section including a training node, and an evaluation section including an evaluation node. At a first class node a first set of training data may be captured and at a second class node a second set of training data may be captured. In response to an input received at the training node, a classification model based on the first set of training data and the second set of training data may be trained. Evaluation data may be captured in an evaluation node, and using the trained classification model, classifications for each piece of the evaluation data may be determined. A visual representation of the classification for each piece of the evaluation data may be output for display within the evaluation node.

Abstract: According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a part parameter dictionary module comprising a processor, geometry data for a plurality of geometric structures forming a plurality of parts, wherein the parts are manufactured with an additive manufacturing machine; determining, using the processor of the part parameter dictionary module, a feature set for each geometric structure; generating, using the processor of the part parameter dictionary module, one of a coupon and a coupon set for the feature set; generating an optimized parameter set for each coupon, using the processor of the part parameter dictionary module, via execution of an iterative learning control process for each coupon; mapping, using the processor of the part parameter dictionary module, one or more parameters of the optimized parameter set to one or more features of the feature set; and generating a dictionary of optimized scan parameter sets to fabricate geometric str

Abstract: According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a part parameter dictionary module comprising a processor, geometry data for a plurality of geometric structures forming a plurality of parts, wherein the parts are manufactured with an additive manufacturing machine; determining, using the processor of the part parameter dictionary module, a feature set for each geometric structure; generating, using the processor of the part parameter dictionary module, one of a coupon and a coupon set for the feature set; generating an optimized parameter set for each coupon, using the processor of the part parameter dictionary module, via execution of an iterative learning control process for each coupon; mapping, using the processor of the part parameter dictionary module, one or more parameters of the optimized parameter set to one or more features of the feature set; and generating a dictionary of optimized scan parameter sets to fabricate geometric str

Abstract: The presently-disclosed subject matter relates to crosslinkers, compositions, and methods for trapping a target of interest on a substrate of interest. The methods may be used to inhibit and treat pathogen infection and provide contraception. The methods may be used to trap or separate particles and other substances. The subject matter further relates to methods of identifying and preparing optimal crosslinkers and methods for manipulating targets of interest.

Abstract: A first version and a second version of audio visual content are provided via a disc, a web site or a mobile device application to consumers. The second version has audiovisual content removed from the first version that is not appropriate for a type of viewer, such as children. In addition, a menu is provided that allows a user to select the first version or the second version to play on a device. In addition to or instead of the second version, the menu may allow the user to customize the first version for an intended audience.

Abstract: According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a platform comprising a segmentation module and a processor, a defined geometry for one or more geometric structures forming one or more parts, wherein the parts are manufactured with an additive manufacturing machine; generating a build file including an initial parameter set to fabricate each part; fabricating the part based on the build file; receiving sensor data for the fabricated part; generating a parameter set for each layer that forms the part, via execution of an iterative learning control process for each layer; generating raw power data for each layer that forms the part, using the processor, based on the generated parameter set; applying a noise reduction process to the raw power data; and generating a segmented build file, using the segmentation module, via application of the noise reduction process on the raw power data. Numerous other aspects are provided.

Abstract: The presently-disclosed subject matter relates to crosslinkers, compositions, and methods for trapping a target of interest on a substrate of interest. The methods may be used to inhibit and treat pathogen infection and provide contraception. The methods may be used to trap or separate particles and other substances. The subject matter further relates to methods of identifying and preparing optimal crosslinkers and methods for manipulating targets of interest.

Abstract: According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a part parameter dictionary module comprising a processor, geometry data for a plurality of geometric structures forming a plurality of parts, wherein the parts are manufactured with an additive manufacturing machine; determining, using the processor of the part parameter dictionary module, a feature set for each geometric structure; generating, using the processor of the part parameter dictionary module, one of a coupon and a coupon set for the feature set; generating an optimized parameter set for each coupon, using the processor of the part parameter dictionary module, via execution of an iterative learning control process for each coupon; mapping, using the processor of the part parameter dictionary module, one or more parameters of the optimized parameter set to one or more features of the feature set; and generating a dictionary of optimized scan parameter sets to fabricate geometric str

Abstract: According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a platform comprising a segmentation module and a processor, a defined geometry for one or more geometric structures forming one or more parts, wherein the parts are manufactured with an additive manufacturing machine; generating a build file including an initial parameter set to fabricate each part; fabricating the part based on the build file; receiving sensor data for the fabricated part; generating a parameter set for each layer that forms the part, via execution of an iterative learning control process for each layer; generating raw power data for each layer that forms the part, using the processor, based on the generated parameter set; applying a noise reduction process to the raw power data; and generating a segmented build file, using the segmentation module, via application of the noise reduction process on the raw power data. Numerous other aspects are provided.

Abstract: Providing updated build parameters to an additive manufacturing machine to improve quality of a part manufactured by the machine. Sensor data is received from the additive manufacturing machine during manufacture of the part using a first set of build parameters. The first set of build parameters is received. An evaluation parameter is determined based on the first set of build parameters and the received sensor data. Thermal data is generated based on a thermal model of the part derived from the first set of build parameters. A first algorithm is applied to the received sensor data, the determined evaluation parameter, and the generated thermal data to produce a second set of build parameters, the first algorithm being trained to improve the evaluation parameter. The second set of build parameters is output to the additive manufacturing machine to produce a second part.

Abstract: Systems and methods disclosed herein include systems and methods for delivery of secondary content to a user device. The secondary content may comprise events synchronized to primary content. In a particular embodiment, the process may include: receiving a request from a user device to access secondary content associated with a primary content; determining whether the user device is entitled to receive the secondary content; and if the user device is entitled to receive the secondary content, providing the secondary content to the user device for the user device to play whenever the user watches the primary content. The secondary content comprises at least one of an audio track, and a subtitle file, and the audio track comprises at least one of an alternate language audio track not provided in the primary content, and an audio track with words omitted from the primary content.