Abstract: Embodiments of the disclosed technologies receive a request including a user identifier and metadata associated with a slot available at a user system, remove the user identifier from the request to produce anonymized request data, receive, from a machine learning model, superposition data that correlates with the anonymized request data, send the superposition data for the anonymized request data to a real-time content-to-request matching process, receive, from the real-time content-to-request matching process, an identifier that identifies a content distribution selected based on the superposition data, and initiate the selected content distribution through the network to the slot in response to the request.

Abstract: A method for constructing an artificial intelligence (AI) integrated model is provided, including: obtaining a training dataset, an initial graph network model, and a plurality of base models; then iteratively training the initial graph network model by using training data in the training dataset and the plurality of base models, to obtain a graph network model; and then constructing the AI integrated model based on the graph network model and the plurality of base models, where an input of the graph network model is a graph structure consisting of outputs of the plurality of base models. Since the graph network model considers neighboring nodes of each node in the graph structure when processing the graph structure, the graph network model fully considers differences and correlations between the base models when fusing the outputs of the plurality of base models.

Abstract: A loading module includes a test vehicle, a servo cylinder is mounted on the test vehicle, a piston rod of the servo cylinder is connected with a bearing plate acting on a subgrade surface, an annular loading plate is disposed around the bearing plate, and the loading plate is connected, through a force transmission rod, with a bearing tray on which a counterweight block is placed; a data collecting module includes a second high precision displacement sensor and a plurality of first high precision displacement sensors; a data processing module includes a computer, the computer is connected with a signal input end of the servo cylinder, an output end of the first high precision displacement sensor and an output end of the second high precision displacement sensor respectively, and performs inverse calculation for a dynamic resilient modulus value of a subgrade using a software.

Abstract: Disclosed is a device and method for reducing combustion cone fallout propensity and stabilizing loss of tobacco from ends. Firstly, the right installation positioning hole is moved rightwards to extend the distance of the separation zone. Meanwhile, the vibration mechanism is redesigned so that it is enabled to move toward the lower right of the material groove body. Therefore, there is enough space to install the separation screen in the separation zone. Finally, the separation screen is installed. The tobacco scraps are separated out when they pass through the separation screen, thus reducing cone dropping when the cigarette burns, stabilizing the amount of dropping tobacco at the end, and improving the smoking experience and recognition of customers with regard to cigarette products.

Abstract: One or more computing devices, systems, and/or methods for assessing riskiness of a domain are provided. For example, a content request is received from a content provider service that hosts a website associated with a domain. The content request is evaluated to identify request features. Feature scores are assigned to the request features using labeled feature data. The feature scores are aggregated to generate a content request risk score corresponding to a riskiness of the content request corresponding to fraud, such as domain spoofing. The content request risk score along with other content request risk scores of content requests associated with the content provider service are aggregated to create a content provider risk score corresponding to a riskiness of the content provider service, such as a risk of the domain being fraudulent. The content provider risk score is used to either block or process the content request.

Abstract: Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of impression indications associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine win probabilities and/or expected bid surpluses associated with multiple shaded bid values based upon one or more feature parameters, of the feature parameters, associated with the second set of features. A shaded bid value for submission may be determined based upon the win probabilities and/or the expected bid surpluses.

Abstract: An optical actuator, including: a base; a lens module comprising two first side surfaces opposite each other; and a plurality of shape memory alloy wires forming two wire groups, the two wire groups being disposed on the two first side surfaces, respectively, wherein two ends of each shape memory alloy wire are fixed to a base-end fixing device and a lens-end fixing device, respectively; and the direction of the resultant force acting on the lens module by the two wire groups is consistent with the direction of the optical axis of the lens module, so that the lens module is driven to move along the direction of the optical axis of the lens module by means of expansion and contraction of the shape memory alloy wires of the two wire groups.

Abstract: One or more computing devices, systems, and/or methods for assessing riskiness of a domain are provided. For example, a content request is received from a content provider service that hosts a website associated with a domain. The content request is evaluated to identify request features. Feature scores are assigned to the request features using labeled feature data. The feature scores are aggregated to generate a content request risk score corresponding to a riskiness of the content request corresponding to fraud, such as domain spoofing. The content request risk score along with other content request risk scores of content requests associated with the content provider service are aggregated to create a content provider risk score corresponding to a riskiness of the content provider service, such as a risk of the domain being fraudulent. The content provider risk score is used to either block or process the content request.

Abstract: Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of impression indications associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine win probabilities and/or expected bid surpluses associated with multiple shaded bid values based upon one or more feature parameters, of the feature parameters, associated with the second set of features. A shaded bid value for submission may be determined based upon the win probabilities and/or the expected bid surpluses.

Abstract: The present invention relates to a cut-stem separating and baffling apparatus and a primary air separation apparatus. The cut-stem separating and baffling apparatus includes a cut-stem separating baffle and struts; and the strut includes a base, a hanging panel, and an inner hexagon adjustment bolt, where the base includes a connecting segment and a support segment that are connected to each other, where the connecting segment is located above the cut-stem separating baffle, and the support segment is located behind the cut-stem separating baffle; the hanging panel includes a transverse hanging panel, and a longitudinal hanging panel, and a connecting hanging panel is disposed in a direction of the longitudinal hanging panel toward the cut-stem separating baffle, where the connecting hanging panel is connected to the cut-stem separating baffle; and the connecting segment of the base is connected to the transverse hanging panel using the inner hexagon adjustment bolt.

Abstract: A color matching method and a color matching device are provided, the color matching method includes: S1, determining a target color; S2, judging whether color data of a sample is obtained, if yes, proceeding to step S3, and if not, proceeding to step S5; S3, measuring the color data of the sample and displaying a color on a substrate, and proceeding to step S4; S4, judging whether the color displayed on the substrate is consistent with the target color, and if not, proceeding to step S6, and if yes, proceeding to step S7; S5, selecting a color and emitting light consistent with a chromaticity value of the selected color onto the substrate and proceeding to step S6; S6, adjusting the color displayed on the substrate until an adjusted color displayed on the substrate is consistent with the target color, and proceeding to S7; and S7, storing data.

Abstract: Systems and methods are provided for controlling an online advertising campaign. In one embodiment, a computer-implemented method for controlling an online advertising campaign includes receiving a feedback signal reflecting delivery of the online advertising campaign, and comparing the feedback signal to a delivery reference to generate a campaign level control signal. The method further includes receiving a maximum impression bid price for an inventory unit of the online advertising campaign, the maximum bid price for the at least one inventory unit being set by a user, and calculating, using at least one processor, at least a final bid price based on the maximum bid price, on the campaign level control signal, and on an optimization objective for the online advertising campaign, the optimization objective being set by the user. The method also includes submitting, to an electronic market and based on the calculated final bid price, a bid on an impression from the inventory unit.

Abstract: Systems and methods are provided for controlling an online advertising campaign. In one embodiment, a computer-implemented method for controlling an online advertising campaign includes receiving a feedback signal reflecting delivery of the online advertising campaign, and comparing the feedback signal to a delivery reference to generate a campaign level control signal. The method further includes receiving a maximum impression bid price for an inventory unit of the online advertising campaign, the maximum bid price for the at least one inventory unit being set by a user, and calculating, using at least one processor, at least a final bid price based on the maximum bid price, on the campaign level control signal, and on an optimization objective for the online advertising campaign, the optimization objective being set by the user. The method also includes submitting, to an electronic market and based on the calculated final bid price, a bid on an impression from the inventory unit.

Abstract: Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of impression indications associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine win probabilities and/or expected bid surpluses associated with multiple shaded bid values based upon one or more feature parameters, of the feature parameters, associated with the second set of features. A shaded bid value for submission may be determined based upon the win probabilities and/or the expected bid surpluses.

Abstract: One or more computing devices, systems, and/or methods for assessing riskiness of a domain are provided. For example, a content request is received from a content provider service that hosts a website associated with a domain. The content request is evaluated to identify request features. Feature scores are assigned to the request features using labeled feature data. The feature scores are aggregated to generate a content request risk score corresponding to a riskiness of the content request corresponding to fraud, such as domain spoofing. The content request risk score along with other content request risk scores of content requests associated with the content provider service are aggregated to create a content provider risk score corresponding to a riskiness of the content provider service, such as a risk of the domain being fraudulent. The content provider risk score is used to either block or process the content request.

Abstract: An optical actuator (302), comprising: a base (100); a lens module (202) comprising two first side surfaces (204) opposite each other; and a plurality of shape memory alloy wires (203) forming two wire groups, the two wire groups being disposed on the two first side surfaces (204), respectively, wherein two ends of each shape memory alloy wire (203) are fixed to a base-end fixing device (206) and a lens-end fixing device (207), respectively; and the direction of the resultant force acting on the lens module (202) by the two wire groups is consistent with the direction of the optical axis of the lens module (202), so that the lens module (202) is driven to move along the direction of the optical axis of the lens module (202) by means of expansion and contraction of the shape memory alloy wires (203) of the two wire groups.

Abstract: One or more computing devices, systems, and/or methods for assessing riskiness of a domain are provided. For example, a content request is received from a content provider service that hosts a website associated with a domain. The content request is evaluated to identify request features. Feature scores are assigned to the request features using labeled feature data. The feature scores are aggregated to generate a content request risk score corresponding to a riskiness of the content request corresponding to fraud, such as domain spoofing. The content request risk score along with other content request risk scores of content requests associated with the content provider service are aggregated to create a content provider risk score corresponding to a riskiness of the content provider service, such as a risk of the domain being fraudulent. The content provider risk score is used to either block or process the content request.

Abstract: Systems and methods are provided for controlling an online advertising campaign. In one embodiment, a computer-implemented method for controlling an online advertising campaign includes receiving a feedback signal reflecting delivery of the online advertising campaign, and comparing the feedback signal to a delivery reference to generate a campaign level control signal. The method further includes receiving a maximum impression bid price for an inventory unit of the online advertising campaign, the maximum bid price for the at least one inventory unit being set by a user, and calculating, using at least one processor, at least a final bid price based on the maximum bid price, on the campaign level control signal, and on an optimization objective for the online advertising campaign, the optimization objective being set by the user. The method also includes submitting, to an electronic market and based on the calculated final bid price, a bid on an impression from the inventory unit.