Abstract: Embodiments relate generally to masks, such as filtration masks, with multiple strap configurations. For example, such masks might be worn in either a head strap or an ear strap configuration. Embodiments typically comprise two ear straps each with a connector support element allowing removable attachment of a separate head strap.

Abstract: A mask (100), such as a filtration mask, with multiple strap configurations is provided. For example, such mask might be worn in either a head strap (240) or an ear strap (110) configuration. The mask comprises two ear straps each with a connector support element (130) allowing removable attachment of a separate head strap.

Abstract: Certain embodiments of the present application relate to an unmanned aerial vehicle including a control system, at least one rotor operable to generate lift under control of the control system, a spool mounted for rotation in a winding direction and an unwinding direction opposite the winding direction, a motor operable to rotate the spool in at least the winding direction under control of the control system, and a brake mechanism including a brake actuator operable to transition the brake mechanism between a locked state and an unlocked state under control of the control system. The brake mechanism in the locked state prevents rotation of the spool in at least the unwinding direction. The brake mechanism in the unlocked state does not prevent rotation of the spool in the unwinding direction.

Abstract: Certain embodiments of the present application relate to a method for releasing a parcel from an unmanned aerial vehicle (UAV). The parcel is engaged with a hook attached to a line suspended from the UAV. The method includes lowering the hook such that the parcel is at least partially supported by a target surface, thereby performing an attempt to release the parcel from the hook, performing at least one iteration of an iterative procedure. The iterative procedure includes raising the hook, and determining whether the line remains loaded. The iterative procedure further includes lowering the hook an additional time in response to a determination that the line remains loaded, thereby performing an additional attempt to release the parcel from the hook. The iterative procedure further includes performing an additional iteration of the iterative procedure in response to the number of attempts failing to satisfy a threshold number of attempts.

Abstract: Certain embodiments of the present application relate to an unmanned aerial vehicle, comprising a chassis, a control system mounted to the chassis, at least one rotor operable to generate lift under control of the control system, a spool having a wound portion of a line wound thereon, the line including a free portion, an attachment device secured to the free portion of the line and configured to facilitate attachment of a parcel to the line, a motor operable to rotate the spool under control of the control system such that the control system is operable to raise the attachment device to a raised position and lower the attachment device from the raised position, and a spring positioned between the chassis and the attachment device such that the spring is engaged between the chassis and the attachment device when the attachment device is in the raised position.

Abstract: Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a first embedding generated by a topic model from parameters of searches by a first recruiting entity and obtains a set of additional embeddings generated by the topic model from attributes of a set of candidates. Next, the system determines match features that include measures of similarity between the first embedding and each embedding in the set of additional embeddings. The system then applies a machine learning model to the match features and additional features for the candidates to produce a set of scores for the candidates. Finally, the system generates a ranking of the candidates according to the scores and outputs at least a portion of the ranking as search results of a current search by the first recruiting entity.

Abstract: Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a first embedding generated by a topic model from parameters of searches by a first recruiting entity and obtains a set of additional embeddings generated by the topic model from attributes of a set of candidates. Next, the system determines match features that include measures of similarity between the first embedding and each embedding in the set of additional embeddings. The system then applies a machine learning model to the match features and additional features for the candidates to produce a set of scores for the candidates. Finally, the system generates a ranking of the candidates according to the scores and outputs at least a portion of the ranking as search results of a current search by the first recruiting entity.

Abstract: A harness may be used to carry a breathable air system. Generally, the harness may comprise at least one shoulder strap and a plurality of attachments configured to attach the at least one shoulder strap to the breathable air system. Additionally, the plurality of attachments may allow the breathable air system to be configured into a plurality of strap configurations. For example, the plurality of strap configurations may include a hip configuration, a waist configuration, a leg configuration, and/or a back configuration. In some configurations, an additional/accessory strap may be used to more securely carry the breathable air system in the plurality of strap configurations.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system identifies a positive action by an entity on a candidate as a result of a query performed by the entity for a ranking of candidates. Next, the system identifies related queries that occur within a time window preceding the query. The system then generates positive labels associated with the candidate and one or more related queries that produce rankings containing the candidate. Finally, the system outputs the positive labels in training data for a machine learning model that generates the rankings.

Abstract: Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.

Abstract: Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.

Abstract: A routable electroforming substrate for assembling a semiconductor package is manufactured by providing a carrier and plating a patterned first metallic layer onto the carrier which is configured to function as a surface mount pad or input/output pad in an assembled semiconductor package. A patterned second metallic layer comprising copper is plated over the first metallic layer, and a third metallic layer, which is configured for mounting a plurality of semiconductor dice, is plated over the second metallic layer. The carrier is then removed to expose the first metallic layer.

Abstract: Systems and methods for identifying user information from a set of pages are disclosed. In example embodiments, a server determines that a first set of pages is associated with a specific user based on addresses of the first set of pages having a common portion of a uniform resource locator (URL). The server determines that at least a threshold number of pages from the first set of pages include common information, the common information comprising contact information or social networking information. The server associates the contact information or the social networking information with a user profile of the specific user. The server provides, as a digital transmission, the contact information or the social networking information.

Abstract: Systems and methods for identifying user information from a set of pages are disclosed. In example embodiments, a server determines that a first set of pages is associated with a specific user based on addresses of the first set of pages having a common portion of a uniform resource locator (URL). The server determines that at least a threshold number of pages from the first set of pages include common information, the common information comprising contact information or social networking information. The server associates the contact information or the social networking information with a user profile of the specific user. The server provides, as a digital transmission, the contact information or the social networking information.

Abstract: A mask (100), such as a filtration mask, with multiple strap configurations is provided. For example, such mask might be worn in either a head strap (240) or an ear strap (110) configuration. The mask comprises two ear straps each with a connector support element (130) allowing removable attachment of a separate head strap.

Abstract: Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.