Abstract: A driving circuit, a driving method, a display substrate and a display device are provided. The driving circuit includes a first node control circuit, a second node control circuit, a third node control circuit and an output circuit; the first node control circuit controls the potential of the first node; the second node control circuit controls the potential of the second node; the third node control circuit controls, under the control of the on-off control signal, the first node to be electrically connected to the third node or the first node to be electrically disconnected from the third node; the output circuit controls, under the control of the potential of the second node and the potential of the third node, output of the driving signal through the driving signal terminal.

Abstract: A method and a system for making contextual recommendations to users on a network-based system. For example, activity associated with a user interacting with a network-based system is tracked. Based, at least in part, on the tracked user activity on the network-based system, a recommendation relationship type is selected. The recommendation relationship type can be either a substitute relationship type or a complement relationship type. A recommended object can be selected based at least in part on the recommendation relationship type and a first object accessed by the user interacting with the network-based system. A recommendation can be generated for the recommended object for presentation to the user interacting with the network-based system.

Abstract: A categorization analysis system is provided. The categorization analysis system includes one or more hardware processors, a memory including a first plurality of listings categorized in a first target category, and a categorization analysis engine executing on the one or more hardware processors. The categorization analysis engine is configured to determine a label for each listing including performing a search on title, select a set of training listings based on the determined labels, train a first model using the set of training listings and the determined labels, the first model being a classification model configured to classify categorization of listings, identify a suspect listing categorized in the first target category, apply the suspect listing to the first model, thereby generating a categorization result for the suspect listing, the categorization result indicating miscategorization of the suspect listing, and identify the suspect listing in the memory as miscategorized.

Abstract: A refinement option recommendation system provides suggestions for completed search queries and refinement options. Upon receiving, at a search engine for a site, a portion of a search query from a user, the refinement option recommendation system determines a completed search query based on the portion of the search query. For example, if the portion includes “bak” or “baking,” the completed search query suggestion may include “baking recipe books” or “baking ingredients.” The refinement option recommendation system also determines a refinement option associated with the completed search query based on historical queries. For example, the refinement option may include recipe books associated with a particular culture based on historical queries by the user or by other users. As such, a first suggestion for the completed search query and a second suggestion for the refinement option are both provided to the user.

Abstract: A categorization analysis system is provided. The categorization analysis system includes one or more hardware processors, a memory including a first plurality of listings categorized in a first target category, and a categorization analysis engine executing on the one or more hardware processors. The categorization analysis engine is configured to determine a label for each listing including performing a search on title, select a set of training listings based on the determined labels, train a first model using the set of training listings and the determined labels, the first model being a classification model configured to classify categorization of listings, identify a suspect listing categorized in the first target category, apply the suspect listing to the first model, thereby generating a categorization result for the suspect listing, the categorization result indicating miscategorization of the suspect listing, and identify the suspect listing in the memory as miscategorized.

Abstract: A categorization analysis system is provided. The categorization analysis system includes one or more hardware processors, a memory including a first plurality of listings categorized in a first target category, and a categorization analysis engine executing on the one or more hardware processors. The categorization analysis engine is configured to determine a label for each listing including performing a search on title, select a set of training listings based on the determined labels, train a first model using the set of training listings and the determined labels, the first model being a classification model configured to classify categorization of listings, identify a suspect listing categorized in the first target category, apply the suspect listing to the first model, thereby generating a categorization result for the suspect listing, the categorization result indicating miscategorization of the suspect listing, and identify the suspect listing in the memory as miscategorized.

Abstract: This application provides example optical communications apparatuses. One example optical communications apparatus includes a control apparatus and an optical module matching apparatus. The control apparatus can output a first control signal to the control end. An input end of the optical module matching apparatus can connect to a first optical module and receive a first electrical signal output by the first optical module. An output end of the optical module matching apparatus can output a first serial signal. The control apparatus can output a second control signal to the control end. The input end of the optical module matching apparatus can receive a second electrical signal output by the second optical module. The output end of the optical module matching apparatus can output a second serial signal. The first electrical signal and the second electrical signal can have different level types.

Abstract: A virtual machine recovery method, where after receiving a virtual machine recovery command for recovering a to-be-recovered virtual machine, a virtual machine management device obtains configuration information of the to-be-recovered virtual machine from a cloud server. Then the virtual machine management device creates, according to the configuration information, a recovered virtual machine and a local storage. After downloading basic system data from the cloud server, the recovered virtual machine is started according to the basic system data. When receiving an input/output (IO) request for accessing a first data of the to-be-recovered virtual machine, the virtual machine management device downloads the first data from the cloud server to the local storage.

Abstract: A categorization analysis system is provided. The categorization analysis system includes one or more hardware processors, a memory including a first plurality of listings categorized in a first target category, and a categorization analysis engine executing on the one or more hardware processors. The categorization analysis engine is configured to determine a label for each listing including performing a search on title, select a set of training listings based on the determined labels, train a first model using the set of training listings and the determined labels, the first model being a classification model configured to classify categorization of listings, identify a suspect listing categorized in the first target category, apply the suspect listing to the first model, thereby generating a categorization result for the suspect listing, the categorization result indicating miscategorization of the suspect listing, and identify the suspect listing in the memory as miscategorized.

Abstract: Techniques for sharing item images based on a similarity score are described. For example, a machine receives a description of an item from a user device. The machine accesses an existing image. The existing image is associated with one or more existing descriptions of the item. The machine selects, based on a description selection rule, the one or more existing descriptions corresponding to the existing image for comparison with the description received from the user device. The machine generates a similarity score for the existing image based on the comparison between the description received from the user device and the one or more existing descriptions corresponding to the existing image. The machine causes a display, in a user interface of the user device, of a number of existing images having corresponding similarity scores that exceed a threshold value.

Abstract: A first variant of a listing and a second variant of the listing are received. The listing describes an item for sale in an electronic marketplace. The first variant describes a different iteration of the item relative to the second variant. First user activity data of the first variant is generated and second user activity data of the second variant is generated. The first user activity data corresponds to user input metrics associated with the first variant. The second user activity data corresponds to user input metrics associated with the second variant. A search engine receives a first query. Based at least in part on the first user activity data relative to the second user activity data, a first search result associated with the first variant is ranked higher than a second search result associated with the second variant.

Abstract: This application provides example optical communications apparatuses. One example optical communications apparatus includes a control apparatus and an optical module matching apparatus. The control apparatus can output a first control signal to the control end. An input end of the optical module matching apparatus can connect to a first optical module and receive a first electrical signal output by the first optical module. An output end of the optical module matching apparatus can output a first serial signal. The control apparatus can output a second control signal to the control end. The input end of the optical module matching apparatus can receive a second electrical signal output by the second optical module. The output end of the optical module matching apparatus can output a second serial signal. The first electrical signal and the second electrical signal can have different level types.

Abstract: A categorization analysis system is provided. The categorization analysis system includes one or more hardware processors, a memory including a first plurality of listings categorized in a first target category, and a categorization analysis engine executing on the one or more hardware processors. The categorization analysis engine is configured to determine a label for each listing including performing a search on title, select a set of training listings based on the determined labels, train a first model using the set of training listings and the determined labels, the first model being a classification model configured to classify categorization of listings, identify a suspect listing categorized in the first target category, apply the suspect listing to the first model, thereby generating a categorization result for the suspect listing, the categorization result indicating miscategorization of the suspect listing, and identify the suspect listing in the memory as miscategorized.

Abstract: A virtual machine recovery method, where after receiving a virtual machine recovery command for recovering a to-be-recovered virtual machine, a virtual machine management device obtains configuration information of the to-be-recovered virtual machine from a cloud server. Then the virtual machine management device creates, according to the configuration information, a recovered virtual machine and a local storage. After downloading basic system data from the cloud server, the recovered virtual machine is started according to the basic system data. When receiving an input/output (IO) request for accessing a first data of the to-be-recovered virtual machine, the virtual machine management device downloads the first data from the cloud server to the local storage.

Abstract: A method and a system for making contextual recommendations to users on a network-based system. For example, activity associated with a user interacting with a network-based system is tracked. Based, at least in part, on the tracked user activity on the network-based system, a recommendation relationship type is selected. The recommendation relationship type can be either a substitute relationship type or a complement relationship type. A recommended object can be selected based at least in part on the recommendation relationship type and a first object accessed by the user interacting with the network-based system. A recommendation can be generated for the recommended object for presentation to the user interacting with the network-based system.

Abstract: A virtual machine recovery method, where after receiving a virtual machine recovery command for recovering a to-be-recovered virtual machine, a virtual machine management device obtains configuration information of the to-be-recovered virtual machine from a cloud server. Then the virtual machine management device creates, according to the configuration information, a recovered virtual machine and a local storage. After downloading basic system data from the cloud server, the recovered virtual machine is started according to the basic system data. When receiving an input/output (IO) request for accessing a first data of the to-be-recovered virtual machine, the virtual machine management device downloads the first data from the cloud server to the local storage. Hence, virtual machine recovery efficiency can be improved.

Abstract: A method and a system for making contextual recommendations to users on a network-based system. For example, activity associated with a user interacting with a network-based system is tracked. Based, at least in part, on the tracked user activity on the network-based system, a recommendation relationship type is selected. The recommendation relationship type can be either a substitute relationship type or a complement relationship type. A recommended object can be selected based at least in part on the recommendation relationship type and a first object accessed by the user interacting with the network-based system. A recommendation can be generated for the recommended object for presentation to the user interacting with the network-based system.

Abstract: A categorization analysis system is provided. The categorization analysis system includes one or more hardware processors, a memory including a first plurality of listings categorized in a first target category, and a categorization analysis engine executing on the one or more hardware processors. The categorization analysis engine is configured to determine a label for each listing including performing a search on title, select a set of training listings based on the determined labels, train a first model using the set of training listings and the determined labels, the first model being a classification model configured to classify categorization of listings, identify a suspect listing categorized in the first target category, apply the suspect listing to the first model, thereby generating a categorization result for the suspect listing, the categorization result indicating miscategorization of the suspect listing, and identify the suspect listing in the memory as miscategorized.

Abstract: A virtual machine recovery method, where after receiving a virtual machine recovery command for recovering a to-be-recovered virtual machine, a virtual machine management device obtains configuration information of the to-be-recovered virtual machine from a cloud server. Then the virtual machine management device creates, according to the configuration information, a recovered virtual machine and a local storage. After downloading basic system data from the cloud server, the recovered virtual machine is started according to the basic system data. When receiving an input/output (IO) request for accessing a first data of the to-be-recovered virtual machine, the virtual machine management device downloads the first data from the cloud server to the local storage. Hence, virtual machine recovery efficiency can be improved.

Abstract: A categorization analysis system is provided. The categorization analysis system includes one or more hardware processors, a memory including a first plurality of listings categorized in a first target category, and a categorization analysis engine executing on the one or more hardware processors. The categorization analysis engine is configured to determine a label for each listing including performing a search on title, select a set of training listings based on the determined labels, train a first model using the set of training listings and the determined labels, the first model being a classification model configured to classify categorization of listings, identify a suspect listing categorized in the first target category, apply the suspect listing to the first model, thereby generating a categorization result for the suspect listing, the categorization result indicating miscategorization of the suspect listing, and identify the suspect listing in the memory as miscategorized.