Abstract: The present disclosure relates to the technical field of compensation for projection thermal defocusing, and the embodiments specifically disclose a bidirectional compensation method and apparatus for projection thermal defocusing, and a readable storage medium.

Abstract: The present application relates to the field of projection technology and discloses a phosphor wheel, a light source module, and a projector. A phosphor wheel includes a base, a first phosphor layer, and a second phosphor layer. The base includes an excitation light reflecting section and an excitation light transmitting section. The first phosphor layer is disposed on a surface of the excitation light reflecting section facing a direction of incident light and spreading along a circumferential direction of the base. The second phosphor layer is disposed on a surface of the excitation light transmitting section facing away from the direction of incident light and spreading along a circumferential direction of the base. The first phosphor layer and the second phosphor layer are offset to each other in a radial direction of the base.

Abstract: Systems, methods, and computer-readable storage media that may be used to generate a Bayesian hierarchical model. One method includes generating a plurality of geographic regions by grouping one or more geographic sub-regions into each of the plurality of geographic regions. The method further includes receiving data for the geographic sub-regions, the data including responses, content inputs, content types, and location identifiers. The method further includes generating geo-level data from the received data by grouping the responses and content inputs of the received data based on a correlation of the location identifiers of the received data to the plurality of geographic regions. The method includes fitting a Bayesian hierarchical model based on at least the geo-level data, the content types, and the geographic regions and determining a content input mix for the content types for each geographic region based on the Bayesian hierarchical model and a content input constraint.

Abstract: Systems, methods, and computer-readable storage media that may be used to generate a Bayesian hierarchical model. One method includes generating a plurality of geographic regions by grouping one or more geographic sub-regions into each of the plurality of geographic regions. The method further includes receiving data for the geographic sub-regions, the data including responses, content inputs, content types, and location identifiers. The method further includes generating geo-level data from the received data by grouping the responses and content inputs of the received data based on a correlation of the location identifiers of the received data to the plurality of geographic regions. The method includes fitting a Bayesian hierarchical model based on at least the geo-level data, the content types, and the geographic regions and determining a content input mix for the content types for each geographic region based on the Bayesian hierarchical model and a content input constraint.

Abstract: Embodiments disclosed provide technical details on fractional attribution using online content provision information. More specifically, embodiments disclosed herein use historical data to determine one or more conditional probabilities and assign credit weights to given events. In this way, more accurate attribution of conversions to particular events may be assigned.

Abstract: Embodiments disclosed provide new approaches for determining fractional attribution using aggregate advertising information. A channel weighting approach may derive the causal influence weight of any channel on conversions. In some embodiments, the approach may include arranging the conversion rate of each channel into different funnel stages, constructing aggregate-level data, and running a multi-stage regression computation using instrumental variables. This approach works with any number of different types of advertising channels, including online and offline channels, and provides the most accurate credit to each channel or sub-channel involved.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: A computer readable information recording medium stores a preview display program that performs, when executed by one or more processors, functions of a part configured to determine setting information for imposition type; a part configured to determine setting information for finishing; and a part configured to display preview images for a state after bookbinding in a case where the setting information for imposition type, determined by the part configured to determine setting information for imposition type, includes a setting of magazine or mini book and the setting information for finishing, determined by the part configured to determine setting information for finishing, includes a setting for carrying out the bookbinding, and display preview images for a state before the bookbinding in the other cases.

Abstract: Embodiments provide fractional attribution using aggregate-level information as well as user-level data. For example, aggregate data may be used to determine marginal conversion probabilities for individual attributes within each channel. For channels that have user-level data, the marginal conversion probabilities may be determined using user-level data associated with converted users and aggregate-level data associated with non-converting users. Different channels may have different attributes and the channels may be weighted, in one embodiment, via a causal analysis using instrumental variables. Each conversion path may be characterized by a set of attributes. Additionally, each conversion path may have touch points. The marginal conversion probabilities for the attributes may be combined to produce an importance weight for each touch point on a converting path. These importance weights can be normalized across the touch points on the converting path to obtain attribution results.

Abstract: Embodiments disclosed provide technical details on fractional attribution using online advertising information. More specifically, embodiments disclosed herein use historical data to determine one or more conditional probabilities and assign credit weights to given events. In this way, fairer and more accurate attribution of conversions to particular events may be assigned.

Abstract: A method for charging multiple battery packs is disclosed, which comprises the steps of providing full output power of the power conversion unit to a first battery pack in a charging cycle at the control of a first control signal; monitoring in real-time the charging acceptance of the first battery pack and when the charging acceptance detected is less than the available full output power of the power conversion unit, charging a second battery pack by the remaining output power, i.e. the output power difference between the full available output power and the charging acceptance of the first patent pack, at the control of a second control signal. The service life of battery is extended and the efficiency of the PV module is increased in the system where in the method is implemented. A device for charging multiple battery packs is also disclosed.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: To determine pricing recommendations for goods and service products in a business-to-business environment, a set of transaction data corresponding to a set of products are processed to generate a set of pricing recommendations optimized according to an objective. Furthermore, a set of product segments may be determined, transaction data may be associated with one or more of the product segments and a demand model and associated price elasticity may be formulated for one or more of the product segments based upon the transaction data associated with the segment. Using these formulated price elasticities, pricing recommendations for each product may be determined for each of a set of customers. Using an optimization process, price elasticities are used to determine price dependent entity goals for any combination of products, customers and sets of prices, using a mathematical objective function.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: A computer readable information recording medium stores a preview display program that performs, when executed by one or more processors, functions of a part configured to determine setting information for imposition type; a part configured to determine setting information for finishing; and a part configured to display preview images for a state after bookbinding in a case where the setting information for imposition type, determined by the part configured to determine setting information for imposition type, includes a setting of magazine or mini book and the setting information for finishing, determined by the part configured to determine setting information for finishing, includes a setting for carrying out the bookbinding, and display preview images for a state before the bookbinding in the other cases.

Abstract: A method for charging multiple battery packs is disclosed, which comprises the steps of providing full output power of the power conversion unit to a first battery pack in a charging cycle at the control of a first control signal; monitoring in real-time the charging acceptance of the first battery pack and when the charging acceptance detected is less than the available full output power of the power conversion unit, charging a second battery pack by the remaining output power, i.e. the output power difference between the full available output power and the charging acceptance of the first patent pack, at the control of a second control signal. The service life of battery is extended and the efficiency of the PV module is increased in the system where in the method is implemented. A device for charging multiple battery packs is also disclosed.

Abstract: A system and method for generating test patterns for a pattern sensitive algorithm. The method comprises the steps extracting feature samples from a layout design; grouping feature samples into clusters; selecting at least one area from the layout design that covers a feature sample from each cluster; and saving each pattern layout covered by the at least one area as test patterns.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the pre-processed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive mode. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: Disclosed are methods and apparatus for analyzing campaigns in order to identify similar campaigns are disclosed. In one embodiment, an ad campaign associated with an advertiser is identified. Ad campaign information associated with ad campaigns previously booked by an online publisher is analyzed to identify one or more of the ad campaigns previously booked by the online publisher that are similar to the ad campaign. The ad campaign information for each of the ad campaigns identifies one or more products of the online publisher. The ad campaign information may be processed by applying natural language processing (NLP) to at least a portion of the ad campaign information associated with the ad campaigns previously booked by the online publisher. At least one of the products of the online publisher to recommend to the advertiser are ascertained from the ad campaign information for the one or more ad campaigns previously booked by the online publisher that are similar to the ad campaign.