Abstract: A second data source may retrieve metadata for one or more versions of a set of versions of a file stored at the first data source. In some examples, the metadata for the one or more versions of the file may include at least an identifier of the file, a timestamp, and a cryptographic signature. In some examples, generation of the cryptographic signature may be based on the identifier of the file, the timestamp, and a cryptographic key. The second data source may identify a set of versions of the file that were uploaded from a trusted data source to the first data source based on a comparison of the cryptographic signature to a computed cryptographic signature. The second data source may then determine a targeted version of the file and retrieve the targeted version of the file from the first data source.

Abstract: A data management system (DMS) may receive entries corresponding to files and directories of a storage system that changed within a time period. The received entries may include metadata associated with the changed files and directories. The DMS may identify one or more directories that were renamed or deleted within the time period by analyzing the metadata from the received entries. The DMS may identify descendent directories and descendent files corresponding to the one or more renamed or deleted directories. The DMS may update path identifiers associated with the descendent directories and the descendent files based on the metadata from the received entries. The DMS may generate an updated index file based on updating the path identifiers for the descendent directories and the descendent files. The described techniques may enable the DMS to identify renames and deletions with greater efficiency, among other benefits.

Abstract: A method is used to manufacture a frame of a curved surgical stapler. The method includes manufacturing a first portion of the frame of the curved surgical stapler separate from the first portion. The first portion includes a first curvilinear portion of an end effector and a first alignment feature. The method also includes manufacturing a second portion of the frame of the curved surgical stapler. The second portion includes a second alignment feature. The method also includes manufacturing a third portion of the frame of the curved surgical stapler separate from either of the first or second portions. The third portion includes a C-shaped track. The method also includes aligning the first and second portions portion with the second portion by aligning the first and second alignment features. The method also includes coupling the first and second portions of the frame of the curved surgical stapler together.

Abstract: In one example embodiment, a server that is in communication with a network that includes a plurality of network elements obtains, from the network, a service request record that includes sensitive information related to at least one of the plurality of network elements. The server parses the service request record to determine that the service request record includes a sequence of characters that is repeated in the service request record, and tags the sequence of characters as a particular sensitive information type. Based on the tagging, the server identically replaces the sequence of characters so as to preserve an internal consistency of the service request record. After identically replacing the sequence of characters, the server publishes the service request record for analysis without revealing the sequence of characters.

Abstract: Thermoplastic compositions include: a copolyester elastomer including a polybutylene terephthalate (PBT) moiety and an elastomer moiety: and from greater than 0.1 wt % to 9 wt % of at least one brightening agent. The copolyester elastomer is formed according to a process including (1) forming the PBT moiety from a high purity terephthalic moiety: and (2) polymerizing the PBT moiety with the elastomer moiety to form the copolyester elastomer, wherein the high purity terephthalic moiety is derived from post-consumer, pre-consumer or post-industrial recycled (PCR) polyethylene terephthalate (PET). The PBT moiety includes PBT derived from post-consumer, pre-consumer or post-industrial recycled (PCR) polyethylene terephthalate (PET), the high purity terephthalic moiety has a purity of at least about 95%, and the thermoplastic composition has an L* color value of at least 80 as determined according to a CIELab color space standard.

Abstract: In one example embodiment, a server that is in communication with a network that includes a plurality of network elements obtains, from the network, a service request record that includes sensitive information related to at least one of the plurality of network elements. The server parses the service request record to determine that the service request record includes a sequence of characters that is repeated in the service request record, and tags the sequence of characters as a particular sensitive information type. Based on the tagging, the server identically replaces the sequence of characters so as to preserve an internal consistency of the service request record. After identically replacing the sequence of characters, the server publishes the service request record for analysis without revealing the sequence of characters.

Abstract: A second data source may retrieve metadata for one or more versions of a set of versions of a file stored at the first data source. In some examples, the metadata for the one or more versions of the file may include at least an identifier of the file, a timestamp, and a cryptographic signature. In some examples, generation of the cryptographic signature may be based on the identifier of the file, the timestamp, and a cryptographic key. The second data source may identify a set of versions of the file that were uploaded from a trusted data source to the first data source based on a comparison of the cryptographic signature to a computed cryptographic signature. The second data source may then determine a targeted version of the file and retrieve the targeted version of the file from the first data source.

Abstract: A data management system (DMS) may receive entries corresponding to files and directories of a storage system that changed within a time period. The received entries may include metadata associated with the changed files and directories. The DMS may identify one or more directories that were renamed or deleted within the time period by analyzing the metadata from the received entries. The DMS may identify descendent directories and descendent files corresponding to the one or more renamed or deleted directories. The DMS may update path identifiers associated with the descendent directories and the descendent files based on the metadata from the received entries. The DMS may generate an updated index file based on updating the path identifiers for the descendent directories and the descendent files. The described techniques may enable the DMS to identify renames and deletions with greater efficiency, among other benefits.

Abstract: A second data source may retrieve metadata for one or more versions of a set of versions of a file stored at the first data source. In some examples, the metadata for the one or more versions of the file may include at least an identifier of the file, a timestamp, and a cryptographic signature. In some examples, generation of the cryptographic signature may be based on the identifier of the file, the timestamp, and a cryptographic key. The second data source may identify a set of versions of the file that were uploaded from a trusted data source to the first data source based on a comparison of the cryptographic signature to a computed cryptographic signature. The second data source may then determine a targeted version of the file and retrieve the targeted version of the file from the first data source.

Abstract: Thermoplastic compositions include from about 15 wt % to about 99 wt % of a polybutylene terephthalate (PBT) component; and from 0.01 wt % to about 85 wt % of at least one additional component, wherein: the PBT component comprises PBT derived from a post-consumer or post-industrial recycled (PCR) polyethylene terephthalate (PET) depolymerized to a high purity purified terephthalic acid (PTA) monomer and the thermoplastic composition exhibits an L* color value of at least about 74.

Abstract: A second data source may retrieve metadata for one or more versions of a set of versions of a file stored at the first data source. In some examples, the metadata for the one or more versions of the file may include at least an identifier of the file, a timestamp, and a cryptographic signature. In some examples, generation of the cryptographic signature may be based on the identifier of the file, the timestamp, and a cryptographic key. The second data source may identify a set of versions of the file that were uploaded from a trusted data source to the first data source based on a comparison of the cryptographic signature to a computed cryptographic signature. The second data source may then determine a targeted version of the file and retrieve the targeted version of the file from the first data source.

Abstract: The embodiments disclosed herein relate to intelligent configuration of a cloud-service gateway based on a pattern recognition algorithm. A machine-learning model is trained to learn the patterns of correlation among many configuration parameters affecting the performance of the system when processing an observed or estimated workload. Training the model may be performed off-line with performance data observed during experiments performed with a variety of configurations and workloads. Once trained, the model may be used to recommend: (a) new configuration parameter values based on constraints of the system being configured, (b) an amount of work that can be performed at a certain performance level when the system is configured with certain parameter values, or (c) the expected performance level when running a certain workload on the system configured with certain configuration parameter values.

Abstract: A method is used to manufacture a frame of a curved surgical stapler. The method includes manufacturing a first portion of the frame of the curved surgical stapler separate from the first portion. The first portion includes a first curvilinear portion of an end effector and a first alignment feature. The method also includes manufacturing a second portion of the frame of the curved surgical stapler. The second portion includes a second alignment feature. The method also includes manufacturing a third portion of the frame of the curved surgical stapler separate from either of the first or second portions. The third portion includes a C-shaped track. The method also includes aligning the first and second portions portion with the second portion by aligning the first and second alignment features. The method also includes coupling the first and second portions of the frame of the curved surgical stapler together.

Abstract: A method is used to manufacture a frame of a curved surgical stapler. The method includes manufacturing a first portion of the frame of the curved surgical stapler separate from the first portion. The first portion includes a first curvilinear portion of an end effector and a first alignment feature. The method also includes manufacturing a second portion of the frame of the curved surgical stapler. The second portion includes a second alignment feature. The method also includes manufacturing a third portion of the frame of the curved surgical stapler separate from either of the first or second portions. The third portion includes a C-shaped track. The method also includes aligning the first and second portions portion with the second portion by aligning the first and second alignment features. The method also includes coupling the first and second portions of the frame of the curved surgical stapler together.

Abstract: In one embodiment, a computing device scans a plurality of available data sources associated with a profiled identity for an individual, and categorizes instances of the data sources according to recognized terms within the data sources. Once determining whether the profiled identity contributed positively to each categorized instance, categorized instances that have a positive contribution by the profiled identity may be clustered into clusters. The computing device may then rank the clusters based on size of the clusters and frequency of recognized terms within the clusters, and can then infer an expertise of the profiled identity based on one or more best-ranked clusters. The inferred expertise of the profiled identity may then be stored.

Abstract: The embodiments disclosed herein relate to intelligent configuration of a cloud-service gateway based on a pattern recognition algorithm. A machine-learning model is trained to learn the patterns of correlation among many configuration parameters affecting the performance of the system when processing an observed or estimated workload. Training the model may be performed off-line with performance data observed during experiments performed with a variety of configurations and workloads. Once trained, the model may be used to recommend: (a) new configuration parameter values based on constraints of the system being configured, (b) an amount of work that can be performed at a certain performance level when the system is configured with certain parameter values, or (c) the expected performance level when running a certain workload on the system configured with certain configuration parameter values.

Abstract: In one example embodiment, a server that is in communication with a network that includes a plurality of network elements obtains, from the network, a service request record that includes sensitive information related to at least one of the plurality of network elements. The server parses the service request record to determine that the service request record includes a sequence of characters that is repeated in the service request record, and tags the sequence of characters as a particular sensitive information type. Based on the tagging, the server identically replaces the sequence of characters so as to preserve an internal consistency of the service request record. After identically replacing the sequence of characters, the server publishes the service request record for analysis without revealing the sequence of characters.

Abstract: In one example embodiment, a server that is in communication with a network that includes a plurality of network elements obtains, from the network, a service request record that includes sensitive information related to at least one of the plurality of network elements. The server parses the service request record to determine that the service request record includes a sequence of characters that is repeated in the service request record, and tags the sequence of characters as a particular sensitive information type. Based on the tagging, the server identically replaces the sequence of characters so as to preserve an internal consistency of the service request record. After identically replacing the sequence of characters, the server publishes the service request record for analysis without revealing the sequence of characters.

Abstract: The embodiments disclosed herein relate to intelligent configuration of a cloud-service gateway based on a pattern recognition algorithm. A machine-learning model is trained to learn the patterns of correlation among many configuration parameters affecting the performance of the system when processing an observed or estimated workload. Training the model may be performed off-line with performance data observed during experiments performed with a variety of configurations and workloads. Once trained, the model may be used to recommend: (a) new configuration parameter values based on constraints of the system being configured, (b) an amount of work that can be performed at a certain performance level when the system is configured with certain parameter values, or (c) the expected performance level when running a certain workload on the system configured with certain configuration parameter values.

Abstract: The embodiments disclosed herein relate to intelligent configuration of a cloud-service gateway based on a pattern recognition algorithm. A machine-learning model is trained to learn the patterns of correlation among many configuration parameters affecting the performance of the system when processing an observed or estimated workload. Training the model may be performed off-line with performance data observed during experiments performed with a variety of configurations and workloads. Once trained, the model may be used to recommend: (a) new configuration parameter values based on constraints of the system being configured, (b) an amount of work that can be performed at a certain performance level when the system is configured with certain parameter values, or (c) the expected performance level when running a certain workload on the system configured with certain configuration parameter values.