Abstract: A method for connecting an appliance (100) to an external server (120) connected to the internet is provided. The method comprises setting-up, with the appliance, a first appliance access point (AP1) for allowing wireless communication with the appliance using a first wireless technology and a second appliance access point (AP2) for allowing wireless communication with the appliance using a second wireless technology, said first technology comprising Wi-Fi technology, and said second technology being different from said first technology. The method further comprises setting-up a wireless link between a mobile device (130) and a selected appliance access point between said first and said second appliance access points (AP1; AP2).

Abstract: Tamper-respondent assemblies are provided which include individualized fluid chemical mixtures for tamper detection. The tamper-respondent assemblies include, for instance, a circuit board and an enclosure. The circuit board includes an electronic component, and the enclosure is mounted to the circuit board to enclose the electronic component within a secure volume, where the secure volume is a sealed space. A fluid is disposed within the secure volume that includes a chemical mixture of multiple chemical substances, and the multiple chemical substances have randomly generated concentrations within the chemical mixture. One or more sensors are provided to facilitate detecting a change in the chemical mixture of the fluid within the secure volume indicative of a tamper event.

Abstract: A sealed speaker enclosure for a bass speaker that is hand-insertable and removable from a hard-side motorcycle saddlebag. The body of the enclosure complements the interior space of the saddlebag making sealing contact with the interior walls of the saddlebag. The bass speaker faces the motorcycle side of the saddlebag within the shock absorber tower indentation overlapping and opening. A speaker grill overlays the opening in the shock absorber tower indentation of the saddlebag.

Abstract: Methods of processing coated articles, such as transparencies, are provided comprising flash annealing one or more layers of the coated article. The one or more layers may be reflective metallic layers, such as silver layers, or comprise a transparent conductive oxide, such as indium tin oxide, or a semiconductor.

Abstract: Example methods and apparatus can be used to reduce noise in motor assemblies, such as those used in architectural coverings. An example apparatus includes a first driver configured to be coupled to and rotated by an output shaft of a motor, a second driver, and a plurality of vibration-absorbers disposed between the first driver and the second driver. The second driver is configured to be coupled to a rotating member to transfer rotational motion from the first driver to the rotating member via the vibration-absorbers.

Abstract: An object store service is operated as a front-end service that stores objects of multiple buckets as objects of multiple back-end object stores. The object store service receives an object to be stored at a particular bucket of the multiple buckets. An object store operation is issued to a particular back-end object store of the multiple of back-end object stores having the particular bucket. The object store operation includes one or more characteristics to be associated with the object stored at the particular bucket.

Abstract: Implementations disclosed describe an inspection device capable of being transferred by a robot blade into a processing chamber of a manufacturing machine, the inspection device comprising an optical sensor to detect light reflected from a target located within the processing chamber, wherein the optical sensor is to output, to a processing device, a signal representative of a state of a region of a surface of the target.

Abstract: A radioactive thermogel suspension, including a thermogel and a plurality of insoluble radioactive isotope phosphate particles, such as yttrium phosphate, suspended in the thermogel. The thermogel is PLGA-g-PEG. The thermogel contains less than 65 ppm stannous octanoate. The plurality of radioactive yttrium phosphate particles are between 0.03 ?m and 10 ?m in diameter. The plurality of radioactive yttrium phosphate particles are generally spherical. The YPO4 particle concentration is in the range of 3 mg/ml to 100 mg/ml.

Abstract: A hyperscale artificial intelligence and machine learning infrastructure includes a plurality of racks, where: at least one or more of the racks include one or more GPU servers; at least one or more of the racks include one or more storage systems; each of the racks include one or more switches coupled to at least one switch in another rack; and the one or more GPU servers are configured to execute one or more artificial intelligence or machine learning applications, wherein data stored within the one or more storage systems is used as input to the one or more artificial intelligence or machine learning applications.

Abstract: An object store service is operated as a front-end to a bucket on a first object store, wherein object store operations for the bucket are issued to the first object store. Content of the bucket is migrated from the first object store to a second object store, wherein operations to store additional content to the bucket are issued to the second object store. Subsequent object store operations for the bucket are issued to the second object store upon completion of the migration.

Abstract: Migrating data from disparate object storage environments, including: initiating a migration from a first object store to a second object store; and processing one or more requests for data from the first object store during the migration.

Abstract: Performing disaster recovery in a cloud-based storage system, including: creating, by a storage system a snapshot of a dataset; uploading, from the storage system to a cloud computing environment, the snapshot; storing, by the cloud computing environment, the snapshot; detecting, by the cloud computing environment, that the dataset is not available on the storage system; and creating, by the cloud computing environment using the snapshot that is stored within the cloud computing environment, a cloud-based storage system that includes the dataset.

Abstract: A hyperscale artificial intelligence and machine learning infrastructure includes a plurality of racks, where: at least one or more of the racks include one or more GPU servers; at least one or more of the racks include one or more storage systems; each of the racks include one or more switches coupled to at least one switch in another rack; and the one or more GPU servers are configured to execute one or more artificial intelligence or machine learning applications, wherein data stored within the one or more storage systems is used as input to the one or more artificial intelligence or machine learning applications.

Abstract: Performing disaster recovery in a cloud-based storage system, including: creating, by a storage system a snapshot of a dataset; uploading, from the storage system to a cloud computing environment, the snapshot; storing, by the cloud computing environment, the snapshot; detecting, by the cloud computing environment, that the dataset is not available on the storage system; and creating, by the cloud computing environment using the snapshot that is stored within the cloud computing environment, a cloud-based storage system that includes the dataset.

Abstract: A method for connecting an appliance (100) to an external server (120) connected to the internet is provided. The method comprises setting-up, with the appliance, a first appliance access point (AP1) for allowing wireless communication with the appliance using a first wireless technology and a second appliance access point (AP2) for allowing wireless communication with the appliance using a second wireless technology, said first technology comprising Wi-Fi technology, and said second technology being different from said first technology. The method further comprises setting-up a wireless link between a mobile device (130) and a selected appliance access point between said first and said second appliance access points (AP1; AP2).

Abstract: A dynamic object model system identifies, from a first object data definition file, a first object definition of a first object of a first entity type; and identifies, from a second object data definition file, a second object definition of a second object of a second entity type. The first object definition defines an entity identifier for the first object, and the second object definition includes the entity identifier of the first object. The dynamic object model system determines, based on an object model definition, that objects of the second entity type refer to objects of the first entity type by entity identifier. Based on the object model definition, and based on the second object definition including the entity identifier of the first object, the dynamic object model system establishes a reference relationship between the first object and the second object within an object database.

Abstract: A system for real time correction during a network device provisioning process is provided. The system includes a network device which maintains an access point connection with a user device while simultaneously attempting to establish a connection to a home network. The network device transmits status information regarding the provisioning process to the user device in real time and notifies the user of any failure in the provisioning process via an initial access point connection. The user is then given the opportunity to correct the issue without disconnecting entirely from the network device.

Abstract: A request to make multiple copies of a data item to multiple new data items is received. A portion of the data item is read into a memory. The portion of the data item is written from the memory to distinct storage locations for the multiple new data items.

Abstract: Performing disaster recovery in a cloud-based storage system, including: creating, by a storage system a snapshot of a dataset; uploading, from the storage system to a cloud computing environment, the snapshot; storing, by the cloud computing environment, the snapshot; detecting, by the cloud computing environment, that the dataset is not available on the storage system; and creating, by the cloud computing environment using the snapshot that is stored within the cloud computing environment, a cloud-based storage system that includes the dataset.

Abstract: A hyperscale artificial intelligence and machine learning infrastructure includes a plurality of racks, where: at least one or more of the racks include one or more GPU servers; at least one or more of the racks include one or more storage systems; each of the racks include one or more switches coupled to at least one switch in another rack; and the one or more GPU servers are configured to execute one or more artificial intelligence or machine learning applications, wherein data stored within the one or more storage systems is used as input to the one or more artificial intelligence or machine learning applications.