Abstract: A radio frequency (RF) switch arrangement that improves the voltage handling capacity of a stack of switching elements (e.g., field-effect transistors (FETs)). The RF switch arrangement can include a ground plane and a stack arranged in relation to the ground plane, the stack including a plurality of switching elements coupled in series with one another. The RF switch arrangement can also include a plurality of capacitive elements, each of the plurality of capacitive elements providing a capacitive path across respective terminals of a corresponding one of the plurality of switching elements.

Abstract: A radio frequency (RF) switch arrangement that improves the voltage handling capacity of a stack of switching elements (e.g., field-effect transistors (FETs)). The RF switch arrangement can include a ground plane and a stack arranged in relation to the ground plane, the stack including a plurality of switching elements coupled in series with one another. The RF switch arrangement can also include a plurality of capacitive elements, each of the plurality of capacitive elements providing a capacitive path across respective terminals of a corresponding one of the plurality of switching elements.

Abstract: A user identifies products offered by the user to an online system. The online system identifies a product offered by the user in an image by applying a identification model to the image. If the online system identifies a product in the image with at least a maximum confidence value, the online system automatically tags the post with metadata about the product or suggests a tag to the user. If an object in the image could be one of multiple products, the online system identifies the multiple products to the user, which may be ordered based on confidences of matching the object, allowing the user to select which product is in the image. If the unlisted identifies a product in the image with less than a minimum confidence value, the online system identifies the user's offered products and suggests that the user select a product.

Abstract: An online system receives a content item including an image from a content-providing user and/or receives an interaction with the content item from a viewing user. The online system accesses a machine-learning model that is trained based on a set of images of items associated with an entity and attributes of each image. The online system applies the model to predict a probability that the content item includes an image of an item associated with the entity based on attributes of the image included in the content item. Based on the predicted probability, the online system updates a profile of the user (i.e., the content-providing user and/or the viewing user) to include an affinity for the item. Upon determining an opportunity to present content to the user, the online system selects content for presentation to the user based on the profile and sends the content for presentation to the user.

Abstract: An online system receives a content item including an image from an online system user. The online system accesses and applies a trained item detection model to predict a probability that a region of interest within the image corresponds to an item associated with an entity based on a set of pixel values associated with the region of interest. If the probability is at least a threshold probability, the online system accesses and applies a trained quality prediction model to predict a measure of quality of the image based on a set of attributes of the image. If the measure of quality is at least a threshold measure of quality, the online system includes the content item in a page associated with the entity maintained in the online system and/or adds a set of data associated with the item and/or the entity to the content item.

Abstract: An online system accesses a model trained based on a topic associated with a set of content items and the content of the set of content items. The online system applies the model to predict a probability that each of multiple content items is associated with the topic based on its content and identifies (a) content item(s) associated with at least a threshold probability. The online system retrieves information describing user engagement with the identified content item(s) and determines a value of a performance metric for the topic based on this information. If the value is at least a threshold value and the online system receives content from an entity describing an item associated with the topic, the online system communicates a recommendation to the entity to create a content item describing the item and to add a tag associated with the item upon determining an opportunity to do so.

Abstract: An online system receives a content item including an image from an online system user. The online system accesses and applies a trained item detection model to predict a probability that a region of interest within the image corresponds to an item associated with an entity based on a set of pixel values associated with the region of interest. If the probability is at least a threshold probability, the online system accesses and applies a trained quality prediction model to predict a measure of quality of the image based on a set of attributes of the image. If the measure of quality is at least a threshold measure of quality, the online system includes the content item in a page associated with the entity maintained in the online system and/or adds a set of data associated with the item and/or the entity to the content item.

Abstract: An online system receives a content item including an image from a content-providing user and/or receives an interaction with the content item from a viewing user. The online system accesses a machine-learning model that is trained based on a set of images of items associated with an entity and attributes of each image. The online system applies the model to predict a probability that the content item includes an image of an item associated with the entity based on attributes of the image included in the content item. Based on the predicted probability, the online system updates a profile of the user (i.e., the content-providing user and/or the viewing user) to include an affinity for the item. Upon determining an opportunity to present content to the user, the online system selects content for presentation to the user based on the profile and sends the content for presentation to the user.

Abstract: A radio frequency (RF) switch arrangement that improves the voltage handling capacity of a stack of switching elements (e.g., field-effect transistors (FETs)). The RF switch arrangement can include a ground plane and a stack arranged in relation to the ground plane, the stack including a plurality of switching elements coupled in series with one another. The RF switch arrangement can also include a plurality of capacitive elements, each of the plurality of capacitive elements providing a capacitive path across respective terminals of a corresponding one of the plurality of switching elements.

Abstract: A publishing user identifies a product offered by the user to an online system by providing multiple images of a product viewed at different angles to the online system. The online system applies an identification model to content items obtained from other users to identify the product. From images of the product from the publishing user and content items from other users having at least a threshold confidence of including the product, the online system generates a three-dimensional reconstruction of the product. The online system may subsequently use the three-dimensional reconstruction of the product to display the product to users or to allow other users to identify the product from the online system.

Abstract: A radio frequency (RF) switch arrangement that improves the voltage handling capacity of a stack of switching elements (e.g., field-effect transistors (FETs)). The RF switch arrangement can include a ground plane and a stack arranged in relation to the ground plane, the stack including a plurality of switching elements coupled in series with one another. The RF switch arrangement can also include a plurality of capacitive elements, each of the plurality of capacitive elements providing a capacitive path across respective terminals of a corresponding one of the plurality of switching elements.

Abstract: An online system receives information describing (an) item(s) associated with an entity and a content item including an image. The online system accesses and applies a trained machine-learning model to predict a probability that the content item includes an image of an item associated with the entity. If the probability is at least a threshold probability, a link to a page associated with the item that includes a set of the information describing the item(s) is added to the content item by the online system. Responsive to receiving an interaction with the link from a user presented with the content item, the online system determines a measure of similarity between the item and each additional item based on the information describing the item(s), arranges the set of the information describing the item(s) within the page based on the measure(s) of similarity, and sends the page for display to the user.

Abstract: A publishing user identifies a product offered by the user to an online system by providing multiple images of various products viewed at different angles to the online system. The online system applies an identification model to content items obtained from other users to identify one or more of the products in various content items. From a number of content items obtained from other users that include products offered by the publishing user, the online system compute trends. The trends may be for a specific product or products having one or more common attributes. The online system transmits information about the trends to the publishing user or to other users. The trends may also be used to rank recommendations for products to a specific user, where the trends from numbers of content items including products are weighted by the specific user's preferences.

Abstract: An online system receives information describing (an) item(s) associated with an entity and a content item including an image. The online system accesses and applies a trained machine-learning model to predict a probability that the content item includes an image of an item associated with the entity. If the probability is at least a threshold probability, a link to a page associated with the item that includes a set of the information describing the item(s) is added to the content item by the online system. Responsive to receiving an interaction with the link from a user presented with the content item, the online system determines a measure of similarity between the item and each additional item based on the information describing the item(s), arranges the set of the information describing the item(s) within the page based on the measure(s) of similarity, and sends the page for display to the user.

Abstract: A publishing user identifies a product offered by the user to an online system by providing multiple images of various products viewed at different angles to the online system. The online system applies an identification model to content items obtained from other users to identify one or more of the products in various content items. Based on a product selected by a viewing user, the online system applies the identification model to various content items to identify content items including the selected product and to identify other products included in content items including the selected product. The online system provides information identifying the other products included in content items along with the selected product to the viewing user. The online system may rank other products included in content items along with the selected product and identify other products to the viewing user based on the ranking.

Abstract: A publishing user identifies a product offered by the user to an online system by providing multiple images of various products viewed at different angles to the online system. The online system applies an identification model to content items obtained from other users to identify one or more of the products in various content items. From a number of content items obtained from other users that include products offered by the publishing user, the online system compute trends. The trends may be for a specific product or products having one or more common attributes. The online system transmits information about the trends to the publishing user or to other users. The trends may also be used to rank recommendations for products to a specific user, where the trends from numbers of content items including products are weighted by the specific user's preferences.

Abstract: An online system receives a content item including an image from an online system user. The online system accesses and applies a trained item detection model to predict a probability that a region of interest within the image corresponds to an item associated with an entity based on a set of pixel values associated with the region of interest. If the probability is at least a threshold probability, the online system accesses and applies a trained quality prediction model to predict a measure of quality of the image based on a set of attributes of the image. If the measure of quality is at least a threshold measure of quality, the online system includes the content item in a page associated with the entity maintained in the online system and/or adds a set of data associated with the item and/or the entity to the content item.

Abstract: An online system receives a content item including an image from a content-providing user and/or receives an interaction with the content item from a viewing user. The online system accesses a machine-learning model that is trained based on a set of images of items associated with an entity and attributes of each image. The online system applies the model to predict a probability that the content item includes an image of an item associated with the entity based on attributes of the image included in the content item. Based on the predicted probability, the online system updates a profile of the user (i.e., the content-providing user and/or the viewing user) to include an affinity for the item. Upon determining an opportunity to present content to the user, the online system selects content for presentation to the user based on the profile and sends the content for presentation to the user.

Abstract: A publishing user identifies a product offered by the user to an online system by providing multiple images of a product viewed at different angles to the online system. The online system applies an identification model to content items obtained from other users to identify the product. From images of the product from the publishing user and content items from other users having at least a threshold confidence of including the product, the online system generates a three-dimensional reconstruction of the product. The online system may subsequently use the three-dimensional reconstruction of the product to display the product to users or to allow other users to identify the product from the online system.

Abstract: An online system receives information describing (an) item(s) associated with an entity and a content item including an image. The online system accesses and applies a trained machine-learning model to predict a probability that the content item includes an image of an item associated with the entity. If the probability is at least a threshold probability, a link to a page associated with the item that includes a set of the information describing the item(s) is added to the content item by the online system. Responsive to receiving an interaction with the link from a user presented with the content item, the online system determines a measure of similarity between the item and each additional item based on the information describing the item(s), arranges the set of the information describing the item(s) within the page based on the measure(s) of similarity, and sends the page for display to the user.