Abstract: An online system receives third party source data from a third party system including content feature vector entries and user feature vector entries, each content feature vector entry describing an corresponding user of the third party system, each component in each user feature vector related to a characteristic of the corresponding user. The online system generates a combination score for a target user and a selected content item by computing a combination of the content feature vector entry associated with the selected content item and the user feature vector entry associated with the target user using a combining function, the combination score indicating an estimated increase in value for the third party system when the target user is presented with the selected content item.

Abstract: An online system determines candidate parameter values to be used by a machine learning algorithm to train a machine learning model by saving historical datasets that include historical parameter searches and the performance of prior machine learning models that were trained on the historical parameters. Using the historical datasets, the online system identifies parameter predictors associated with a relation between candidate parameter values and properties of the training dataset that will be used to train the machine learning model. The online system trains the machine learning models according to the candidate parameter values and validates that the machine learning model is performing as expected. If the online system detects that the machine learning model is performing outside of an acceptable range, the online system determines new candidate parameter values and re-trains the machine learning model.

Abstract: An online system uses multiple machine learning models to select content for providing to a user of the online system. Specifically, the online system trains a general model that intakes a first set of features and outputs predictions at a general level. The online system further trains a residual model that intakes a second set of features. The residual model predicts a residual (e.g., an error) of the predictions outputted by the general model. Therefore, the predicted residual from the residual model is combined with the prediction from the general model in order to correct for the over-generality of the general model. The online system may use the combined prediction to send content to users.

Abstract: An online system determines how presenting an awareness campaign to a user will affect the user's likelihood of converting to a related direct response campaign. For the user, the online system creates a benchmark exposure profile representing the user's exposure history before the awareness campaign. Similarly, the online system determines the user's simulated exposure profile, which represents the user's brand exposure history after having been exposed to the awareness campaign. A response prediction for the direct response campaign is determined for the benchmark exposure profile and the simulated exposure profile. The online system estimates the difference between the response prediction and the simulated response prediction to determine a delivery control value of presenting the awareness campaign to a user. The delivery control value is used to determine an effective impression value for the awareness campaign and conversion value for the related direct response campaign.

Abstract: When an opportunity arises to present a content item to a user, an online system delivers a content item to a user according to a first content delivery strategy associated with the content item. For the impression of the content item to the user, the online system tracks attributes associated with the first content delivery strategy. In addition to tracking the attributes associated with the first content delivery strategy, the online system also tracks attributes associated with at least one other content delivery strategy (a second content delivery strategy). The attributes tracked for the second content delivery strategy are used to train a machine learning model for the second content delivery strategy. The model is used to deliver the content item or other items according to the second content delivery strategy.

Abstract: An online system generates predicted outcomes for a content distribution program that distributes content to users of the online system, the predicted outcome indicating a likelihood for the occurrence of an outcome of a content presentation. The online system transmits the one or more predicted outcomes to the third party system, and receives prediction improvement data from the third party system, the prediction improvement data indicating an adjustment to errors in the predicted outcomes based on a prediction by the third party system. The online system updates the properties of a content distribution program based on the prediction improvement data, the updated content distribution program causing the online system to generate new predicted outcomes based on the prediction improvement data in content presentation opportunities. The online system also transmits content to users of the online system based on the updated content distribution program.

Abstract: An online system receives explicit user data and explicit event data, and implicit user data and implicit event data from a third party system. The online system generates an implicit users/implicit events data feature, an explicit users/explicit events data feature, and an explicit users/implicit events data feature. The online system generates a prediction of the counterfactual rate based on the implicit users/implicit events data feature, the explicit users/explicit events data feature, and the explicit users/explicit events data feature, the counterfactual rate indicating the likelihood that target users matching certain characteristics caused an event to occur when the target are not been presented with content by the online system, the content configured to induce users to cause the event to occur. A combined prediction rate is presented to the third party system based on the counterfactual rate.

Abstract: An online system receives third party source data from a third party system including content feature vector entries and user feature vector entries, each content feature vector entry describing an corresponding user of the third party system, each component in each user feature vector related to a characteristic of the corresponding user. The online system generates a combination score for a target user and a selected content item by computing a combination of the content feature vector entry associated with the selected content item and the user feature vector entry associated with the target user using a combining function, the combination score indicating an estimated increase in value for the third party system when the target user is presented with the selected content item.

Abstract: An online system selects content for presentation to a user based on characteristics of the user, such as prior interactions with content by users having similar characteristics. To obtain information about interaction with a content item by users having a broader range of characteristics, the online system may increase an attribute of a content item used to select content based on a measure of dissimilarity between the user and other users who have previously been presented with the content item. The measure of dissimilarity may be determined based on differences between characteristics of the user and characteristics of users presented with the content item weighted by a temporal decay factor. For example, the online system increases an attribute of the content item by an amount directly related to the measure of dissimilarity and uses the increased attribute when determining whether to present the content item to the user.

Abstract: An online system, such as a social networking system, generates shared models for one or more clusters of categories. A shared model for a cluster is common to the categories assigned to the cluster. In this manner, the shared models are specific to the group of categories (e.g., selected content providers) in each cluster while requiring a reasonable computational complexity for the online system. The categories are clustered based on the performance of a model specific to a category on data for other categories.

Abstract: An online system optimizes selection content items for a user based on total value of presenting a selected content item, rather than discrete actions with the content item. To account for the total value of presenting a content item, the online system receives information from a third party system associated with the content item identifying actions by users captured by the third party system and values associated with the identified actions. The online system matches the identified actions with presentations of the content item to various users by identifying users of the online system corresponding to information identifying users received from the third party system and retrieves information describing presentation of content items to the information identifying presentation of content items. Based on historical actions and presentations of a content item, the online system obtains a model determining value of presenting a content item for use in selecting content.

Abstract: An online system identifies an impression opportunity for a target user of the online system. The online system accesses predictors for a third party system, each predictor determining a prediction value indicating a likelihood of users to provide a specified benefit to the third party system after a specified timeframe from the performance of a specified type of action by the users at the online system, each predictor trained using a training feature set extracted from an impressions log including metadata for past impression opportunities made to users. The online system determines a combined bid value for the third party system based on prediction values determined by the predictors trained for the third party system. In response to determining that the combined bid value for the third party system is a winning bid value, the online system presents a sponsored content from the third party system to the user.

Abstract: An online system manages a set of custom features for a third party system stored in user profiles. The online system accesses predictors for the third party system based on the set of custom features for the third party system, the predictors generating predictions for users to the third party system based on the custom features of a lifetime expected incremental value to the third party system from presenting the sponsored content item to the target user. The online system receives from the third party system, data elements for a target user, the data elements related to the actions performed by the target user. The online system extracts custom features from the data elements based on a custom feature definition associated with the third party system. The online system determines a value score for the target user based on the extracted custom features for the target user using the predictors.

Abstract: A method for forming an MRAM bit is described that includes providing a covering layer over an integrated circuit structure. In one embodiment, the covering layer includes tantalum. A first mask layer is formed over the covering layer followed by a second mask layer. The first mask layer and second mask layer are etchable by the same etching process. The first and second mask layer are etched. Etch residue is removed from the first and second mask layers. The first mask layer is then selectively removed and the second mask layer remains.

Abstract: Methods for forming the lower electrode of a capacitor in a semiconductor circuit, and the capacitors formed by such methods are provided. The lower electrode is fabricated by forming a texturizing underlayer and then depositing a conductive material thereover. In one embodiment of a method of forming the lower electrode, the texturizing layer is formed by depositing a polymeric material comprising a hydrocarbon block and a silicon-containing block, over the insulative layer of a container, and then subsequently converting the polymeric film to relief or porous nanostructures by exposure to UV radiation and ozone, resulting in a textured porous or relief silicon oxycarbide film. A conductive material is then deposited over the texturizing layer resulting in a lower electrode have an upper roughened surface.

Abstract: The present invention provides a method of forming an MRAM cell which minimizes the occurrence of electrical shorts during fabrication. A first conductor is provided in a trench in an insulating layer and an upper surface of the insulating layer and the first conductor is planarized. Then, a first dielectric layer is deposited over the first conductor and insulating layer to a thickness at least greater than the thickness of a desired MRAM cell. The first dielectric layer is then patterned and etched to form an opening over the first conductor for the cell shapes. Then, the magnetic layers comprising the MRAM cell are consecutively formed within the cell shapes and the first dielectric layer.

Abstract: Methods for forming the lower electrode of a capacitor in a semiconductor circuit, and the capacitors formed by such methods are provided. The lower electrode is fabricated by forming a texturizing underlayer and then depositing a conductive material thereover. In one embodiment of a method of forming the lower electrode, the texturizing layer is formed by depositing a polymeric material comprising a hydrocarbon block and a silicon-containing block, over the insulative layer of a container, and then subsequently converting the polymeric film to relief or porous nanostructures by exposure to UV radiation and ozone, resulting in a textured porous or relief silicon oxycarbide film. A conductive material is then deposited over the texturizing layer resulting in a lower electrode have an upper roughened surface.

Abstract: The invention includes methods of fabricating integrated circuitry and semiconductor processing polymer residue removing solutions. In one implementation, a method of fabricating integrated circuitry includes forming a conductive metal line over a semiconductor substrate. The conductive line is exposed to a solution comprising an inorganic acid, hydrogen peroxide and a carboxylic acid buffering agent. In one implementation, a method of fabricating integrated circuitry includes forming an insulating layer over a semiconductor substrate. A contact opening is at least partially formed into the insulating layer. The contact opening is exposed to a solution comprising an inorganic acid, hydrogen peroxide and a carboxylic acid buffering agent. In one implementation, a semiconductor processing polymer residue removing solution comprises an inorganic acid, hydrogen peroxide and a carboxylic acid buffering agent. Other aspects and implementations are contemplated.

Abstract: A method for forming an MRAM bit is described that includes providing a covering layer over an integrated circuit structure. In one embodiment, the covering layer includes tantalum. A first mask layer is formed over the covering layer followed by a second mask layer. The first mask layer and second mask layer are etchable by the same etching process. The first and second mask layer are etched. Etch residue is removed from the first and second mask layers. The first mask layer is then selectively removed and the second mask layer remains.

Abstract: A composition and methods for using the composition in removing processing byproducts is provided. The composition can be non-aqueous or semi-aqueous. The non-aqueous composition includes a non-aqueous solvent and one or more components including a fluoride compound and a pyridine compound. The semi-aqueous composition includes glacial acetic acid and one or more components including a fluoride compound and a pyridine compound. The composition can be used in removing processing byproducts from substrate assembly, including MRAM devices, that include at least a metal containing region and processing byproducts, where removing the processing byproducts includes exposing the substrate assembly to the composition for a time effective to remove at least a portion of the processing byproducts.