Abstract: An online system receives information describing a target group of online system users from a third party system that includes one or more user properties, which may identify an action performed by a an online user within a time interval to include the user in the target group. When an opportunity to present advertisement requests to a user is identified, the online system retrieves actions associated with the user and times associated with the actions. If an action associated with the user matches the action identified by the user properties and occurred within the time interval identified by the user properties, and other characteristics of the user satisfy at least a threshold number of the user properties, the online system includes the user in the target group. Advertisement requests having targeting criteria, including the target group, satisfied by characteristics of the user are evaluated for presentation to the user.

Abstract: According to examples, a system for generating and providing organization-bounded spaces for a virtual community of users may include a processor and a memory storing instructions. The processor, when executing the instructions, may cause the system to generate an organization-bounded space; analyze user information to associate a user with the organization-bounded space; enable the user to receive and publish a content item; and implement a security feature for communications associated with an organization-bounded space and/or its associated users. The processor may further determine recommend a group to the user and identify a user representative for the organization-bounded space.

Abstract: According to examples, a system for generating and delivering enhanced content utilizing remote rendering and data streaming is described. The system may include a processor and a memory storing instructions. The processor, when executing the instructions, may cause the system to transmit a selected engagement content item for transmission to a user device and receive an indication of interest relating to the selected engagement content item. The processor, when executing the instructions, may then select, based on the received indication of interest, an enhanced content item, process the enhanced to content item to generate a stream of content data associated with the enhanced content item and transmit the stream of content data associated with the enhanced content item to the user device.

Abstract: Techniques are described that classify content, and control whether and how the content is shared based on the classification(s). In some examples, video content may be classified based on sequential image frames of the video, and time between the sequential image frames. Audio content may be classified based on combining classifications of multiple sound events in the audio signal. The classifications may be used to control how the content is shared, such as by preventing offensive content from being shared and/or outputting recommendations or search results based on the classifications.

Abstract: The disclosed fiber optic cable may include (1) a plurality of optical fibers, (2) a core tube surrounding the plurality of optical fibers, (3) a thixotropic gel filling an interstitial space among the optical fibers within the core tube, (4) an intermediate layer surrounding the core tube, where the intermediate layer includes a plurality of linear elements contra-helically wrapped about the core tube, and (5) an outer layer surrounding the intermediate layer, where the outer layer includes a combination of a moisture-cure cross-linked material and an activation catalyst, where the outer layer is formed by masticating and extruding the combination onto the intermediate layer. Various other cables, assemblies, and methods are also disclosed.

Abstract: The disclosed system may include (1) a conductive coil, where at least a portion of the coil is oriented along a first direction and orthogonal to a second direction, (2) a magnetic field generation structure that generates a magnetic field through the coil along a third direction orthogonal to the first and second directions, (3) a force constant compensator that (a) receives a current command to alter a relative location of the coil and the field, and (b) adjusts the current command based on at least one physical characteristic of the system that affects a relationship between current in the coil and resulting force between the coil and the field along the second direction, and (4) a coil driver that generates, in response to the adjusted current command, a first current in the coil to generate a force between the coil and the field. Other embodiments are also disclosed.

Abstract: The disclosed method may include (1) determining whether a next operation of a plurality of operations of an artificial neural network (ANN) is dependent upon a Boolean predication value based on a representative value for a weight or an input of a node of the ANN, (2) based on the next operation not being dependent on the Boolean predication value, allowing the next operation to update a state of the ANN, and (3) based on the next operation being dependent on the Boolean predication value, performing at least one of (a) allowing, based on the Boolean predication value being a first value, the next operation to update the state of the ANN, and (b) preventing, based on the Boolean predication value being a second value different from the first value, the next operation from updating the state of the ANN. Various other methods and systems are also disclosed.

Abstract: The disclosed computer-implemented method may include determining a set of filter vectors. Each filter vector in the set of filter vectors may include a set of filter weights associated with at least one portion of an output volume of a resampling operation. The method may also include generating, via a clustering algorithm and based on the set of filter vectors, a filter bank for the resampling operation. The filter bank may include an additional set of filter vectors. The method may further include (1) transmitting the filter bank to a memory module included in a hardware accelerator, and (2) directing the hardware accelerator to execute the resampling operation using an input volume and the filter bank. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Systems, methods, and non-transitory computer readable media are configured to provide a first element in an interface presentable to a user through which a plurality of ephemeral media content items are accessible by the user for a selected period of time. A second element in the interface through which a plurality of non-ephemeral media content items are accessible by the user is provided.

Abstract: A computer-implemented method for generating and broadcasting digital trails of visual media may include (i) receiving user input initiating a digital trail, the user input including a selection of a trail mode from a list of candidate modes, (ii) creating a digital trail container configured to contain a series of thematically related visual media works generated by users invited to contribute to the digital trail, (iii) providing, to one or more users, a creation prompt that corresponds to the trail mode, (iv) adding, to the digital trail container, at least one visual media work received in response to providing the creation prompt, and (v) displaying the resulting digital trail by sequentially presenting each visual media work added to the digital trail container in the order added. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A disclosed computing system may include a special-purpose hardware device having an input subsystem, a linearization subsystem, and a matrix multiplication unit. The input subsystem may facilitate on-the-fly convolution lowering within a neural network convolution layer by directing input volume patches to logical unit(s) of the device. The linearization subsystem may be configured to receive a patch from the input subsystem and to linearize the patch by arranging elements of the patch as a portion of a data matrix row. The matrix multiplication unit of device may be configured to receive the data matrix from the linearization subsystem and to apply a filter matrix to the data matrix via a matrix multiplication operation. Various other methods, systems, and computer-readable media are also disclosed.