Abstract: A computer implemented method includes receiving an image that includes a type of object, segmenting the object into multiple segments via a trained segmentation machine learning model, and inputting the segments into multiple different attribute extraction models to extract different types of attributes from each of the multiple segments.

Abstract: A neural network architecture for attention-based efficient model adaptation is disclosed. A method includes accessing an input vector, the input vector comprising a numeric representation of an input to a neural network. The method includes providing the input vector to the neural network comprising a plurality of ordered layers, wherein each layer in at least a subset of the plurality of ordered layers is coupled with an adaptation module, wherein the adaptation module receives a same input value as a coupled layer for the adaptation module, and wherein an output value of the adaptation module is pointwise multiplied with an output value of the coupled layer to generate a next layer input value. The method includes generating an output of the neural network based on an output of a last one of the plurality of ordered layers in the neural network.

Abstract: Examples are disclosed that relate to computing devices and methods for synthesizing a novel pose of an object. One example provides a method comprising receiving a reference image of an object corresponding to an original viewpoint. The reference image of the object is translated into a depth map of the object, and a new depth map of the object is synthesized to correspond to a new viewpoint. A new image of the object is generated from the new viewpoint based on the new depth map of the object and the reference image of the object.

Abstract: Examples are disclosed that relate to computing devices and methods for synthesizing a novel pose of an object. One example provides a method comprising receiving a reference image of an object corresponding to an original viewpoint. The reference image of the object is translated into a depth map of the object, and a new depth map of the object is synthesized to correspond to a new viewpoint. A new image of the object is generated from the new viewpoint based on the new depth map of the object and the reference image of the object.

Abstract: A neural network architecture for attention-based efficient model adaptation is disclosed. A method includes accessing an input vector, the input vector comprising a numeric representation of an input to a neural network. The method includes providing the input vector to the neural network comprising a plurality of ordered layers, wherein each layer in at least a subset of the plurality of ordered layers is coupled with an adaptation module, wherein the adaptation module receives a same input value as a coupled layer for the adaptation module, and wherein an output value of the adaptation module is pointwise multiplied with an output value of the coupled layer to generate a next layer input value. The method includes generating an output of the neural network based on an output of a last one of the plurality of ordered layers in the neural network.

Abstract: A memory allocation system is provided and includes nodes, one or more memories, and an allocation interface. Each of the nodes includes a respective set of processors. The one or more memories include memory elements for storing threads. The memory elements refer to respective portions of the one or more memories and are accessible to at least one of the nodes. The allocation interface is configured to allocate the memory elements to lockless list structures. Each of the lockless list structures is allocated to a respective set of the memory elements. The lockless list structures are partitioned for the processors. The allocation interface is configured to receive requests from the processors for the memory elements and adjust allocation of the memory elements between the lockless list structures according to a balancing metric.

Abstract: A memory allocation system is provided and includes nodes, one or more memories, and an allocation interface. Each of the nodes includes a respective set of processors. The one or more memories include memory elements for storing threads. The memory elements refer to respective portions of the one or more memories and are accessible to at least one of the nodes. The allocation interface is configured to allocate the memory elements to lockless list structures. Each of the lockless list structures is allocated to a respective set of the memory elements. The lockless list structures are partitioned for the processors. The allocation interface is configured to receive requests from the processors for the memory elements and adjust allocation of the memory elements between the lockless list structures according to a balancing metric.

Abstract: Systems and techniques of the management of the allocation of a plurality of memory elements stored within a plurality of lockless list structures are presented. These lockless list structures (such as Slists) may be made accessible within an operating system environment of a multicore processor—and may be partitioned within the system. Memory elements may also be partitioned among these lockless list structures. When a core processor (or other processing element) makes a request for allocating a memory element to itself, the system and/or method may search among the lockless list structures for an available memory element. When a suitable and/or available memory element is found, the system may allocate the available memory element to requesting core processor. Dynamically balancing of memory elements may occur according to a suitable balancing metric, such as maintain substantial numerical equality of memory elements or avoid over-allocation of resources.

Abstract: Systems and techniques of the management of the allocation of a plurality of memory elements stored within a plurality of lockless list structures are presented. These lockless list structures (such as Slists) may be made accessible within an operating system environment of a multicore processor—and may be partitioned within the system. Memory elements may also be partitioned among these lockless list structures. When a core processor (or other processing element) makes a request for allocating a memory element to itself, the system and/or method may search among the lockless list structures for an available memory element. When a suitable and/or available memory element is found, the system may allocate the available memory element to requesting core processor. Dynamically balancing of memory elements may occur according to a suitable balancing metric, such as maintain substantial numerical equality of memory elements or avoid over-allocation of resources.

Abstract: Disclosed are compounds of the formula wherein R1 is hydrogen or an acyl group having 1 to 16 carbon atoms; R2 is a purine or pyrimidine base or an analogue or derivative thereof; Z is O, S, S?O or SO2; and pharmaceutically acceptable derivatives thereof. Also described are processes for and intermediates of use in their preparation, pharmaceutical compositions containing these compounds, and the use of these compounds in the antiviral treatment of mammals.

Abstract: Disclosed are compounds of the formula wherein R1 is hydrogen or an acyl group having 1 to 16 carbon atoms; R2 is a purine or pyrimidine base or an analogue or derivative thereof; Z is O, S, S?O or SO2; and pharmaceutically acceptable derivatives thereof. Also, described are process for and intermediates of use in their preparation, pharmaceutical compositions containing these compounds, and the use of these compounds in the antiviral treatment of mammals.

Abstract: The present invention relates to novel oxo-aminotetralin compounds of the formula (I) wherein X, R1, R2, R3, R4, R5, and R6 are defined herein. The compounds of formula (I) are useful in pain management.

Abstract: Disclosed are compounds of the formula

Abstract: The present invention relates to novel thio-aminotetralin compounds of the formula (I) wherein Z, X, R1, R2, R3, R4, R5, and R6 are defined herein. The compounds are useful in pain management.

Abstract: There is provided novel 2-substituted-4-substituted-1,3-dioxolanes which are particularly useful as antiviral agents.

Abstract: There are provided novel 2-substituted-4-substituted-1,3-dioxolanes which are particularly useful as antiviral agents.