Abstract: A display substrate and a display device are provided. The display substrate includes a base substrate, a pixel circuit layer and an anode layer; the pixel circuit layer includes a plurality of pixel driving circuits, the anode layer includes a plurality of anode groups, each of the plurality of anode groups includes a first anode and a second anode which are oppositely arranged, the first anode includes a first main body portion and a first connection portion, the first anode further includes an extension portion and an anode compensation portion, an orthographic projection of the anode compensation portion on the base substrate covers one thin film transistor in the pixel driving circuit connected to the first connection portion.

Abstract: One or more computing devices, systems, and/or methods for oversampling for imbalanced test data are provided. A classifier is configured to classify data points as either belonging to a first class or a second class. A determination may be made that the first class and the second class are imbalanced where a first number of data points estimated to be part of the first class is a threshold amount less than a second number of data points estimated to be part of the second class. An oversampling ratio is determined for the first class. The oversampling ratio is used to select a sample set of data points for editorial labeling, where the sampling set of data points comprises a total number of data points below a threshold amount.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel messaging framework that automatically applies a multi-factor analysis technique to incoming and received messages in order to properly identify a message's type and category, which dictates the manner in which the message is displayed within a recipient's inbox. The disclosed framework operates on two levels: i) it determines whether a message is from a human or machine sender (H/M classification), and ii) it determines the messages category (MAGMA categorization).

Abstract: Secure multi-party information retrieval is disclosed. One example is a system including a query processor to request secure retrieval of candidate terms similar to a query term. A collection of information processors, where a given information processor receives the request and generates a random permutation. A plurality of data processors, where a given data processor generates clusters of a plurality of terms in a given dataset, where the clusters are based on similarity scores for pairs of terms, and selects a representative term from each cluster. The given information processor determines similarity scores between a secured query term received from the query processor and secured representative terms received from the given data processor, where the secured terms are based on the permutation, and the given data processor filters, without knowledge of the query term, the candidate terms of the plurality of terms based on the determined similarity scores.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel messaging framework that automatically applies a multi-factor analysis technique to incoming and received messages in order to properly identify a message's type and category, which dictates the manner in which the message is displayed within a recipient's inbox. The disclosed framework operates on two levels: i) it determines whether a message is from a human or machine sender (H/M classification), and ii) it determines the messages category (MAGMA categorization).

Abstract: Examples disclosed herein involve data stream analytics. In examples herein, a data stream may be analyzed by computing a set of hashes of a real-valued vector, the real-valued vector corresponding to a sample data object of a data stream; generating a list of data objects from a database corresponding to the sample data object based on the set of hashes, the list of data objects ordered based on similarity of the data objects to the sample data object of the data stream; and updating a data structure representative of activity of the sample data object in the data stream based on the list of data objects, the data structure to provide incremental analysis corresponding to the sample data object.

Abstract: One or more computing devices, systems, and/or methods for oversampling for imbalanced test data are provided. A classifier is configured to classify data points as either belonging to a first class or a second class. A determination may be made that the first class and the second class are imbalanced where a first number of data points estimated to be part of the first class is a threshold amount less than a second number of data points estimated to be part of the second class. An oversampling ratio is determined for the first class. The oversampling ratio is used to select a sample set of data points for editorial labeling, where the sampling set of data points comprises a total number of data points below a threshold amount.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel messaging framework that automatically applies a multi-factor analysis technique to incoming and received messages in order to properly identify a message's type and category, which dictates the manner in which the message is displayed within a recipient's inbox. The disclosed framework operates on two levels: i) it determines whether a message is from a human or machine sender (H/M classification), and ii) it determines the messages category (MAGMA categorization).

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel messaging framework that automatically applies a multi-factor analysis technique to incoming and received messages in order to properly identify a message's type and category, which dictates the manner in which the message is displayed within a recipient's inbox. The disclosed framework operates on two levels: i) it determines whether a message is from a human or machine sender (H/M classification), and ii) it determines the messages category (MAGMA categorization).

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel messaging framework that automatically applies a multi-factor analysis technique to incoming and received messages in order to properly identify a message's type and category, which dictates the manner in which the message is displayed within a recipient's inbox. The disclosed framework operates on two levels: i) it determines whether a message is from a human or machine sender (H/M classification), and ii) it determines the messages category (MAGMA categorization).

Abstract: Incremental automatic update of ranked neighbor lists based on k-th nearest neighbors is disclosed. One example is a system including an indexing module to retrieve an incoming data stream, and retrieve ranked neighbor lists for received data objects. An evaluator determines similarity measures between the received data objects and their respective k-th nearest neighbors. A threshold determination module determines a statistical distribution based on the determined similarity measures, and a threshold based on the statistical distribution. The evaluator determines additional similarity measures between a new data object in the data stream and the received data objects.

Abstract: Automatic selection of neighbor lists to be incrementally updated is disclosed. One example is a system including an indexing module to receive an incoming data stream, and retrieve neighbor lists for received data objects. An evaluator determines similarity measures between pairs of the received data objects. A threshold determination module determines distributions of order statistics based on the determined similarity measures and retrieved neighbor lists, and a threshold based on the distributions of order statistics. The evaluator determines additional similarity measures between a new data object in the data stream and the received data objects.

Abstract: Dynamically updating a ridge regression data model of a continuous stream of data is disclosed. New data chunks corresponding to a current data accumulation point are received and the data values in the new data chunks are transformed via standardization methods. A ridge estimator for the standardized data that includes data chunks received up to a penultimate data accumulation point to include the new data chunks is dynamically updated. The cumulative observations received up to the current data accumulation point are updated and stored. Predictions for the continuous data stream are generated based on the updated ridge estimator.

Abstract: Incremental automatic update of ranked neighbor lists based on k-th nearest neighbors is disclosed. One example is a system including an indexing module to retrieve an incoming data stream, and retrieve ranked neighbor lists for received data objects. An evaluator determines similarity measures between the received data objects and their respective k-th nearest neighbors. A threshold determination module determines a statistical distribution based on the determined similarity measures, and a threshold based on the statistical distribution. The evaluator determines additional similarity measures between a new data object in the data stream and the received data objects.

Abstract: Automatic selection of neighbor lists to be incrementally updated is disclosed. One example is a system including an indexing module to receive an incoming data stream, and retrieve neighbor lists for received data objects. An evaluator determines similarity measures between pairs of the received data objects. A threshold determination module determines distributions of order statistics based on the determined similarity measures and retrieved neighbor lists, and a threshold based on the distributions of order statistics. The evaluator determines additional similarity measures between a new data object in the data stream and the received data objects.

Abstract: Secure multi-party information retrieval is disclosed. One example is a system including a query processor to request secure retrieval of candidate terms similar to a query term. A collection of information processors, where a given information processor receives the request and generates a random permutation. A plurality of data processors, where a given data processor generates clusters of a plurality of terms in a given dataset, where the clusters are based on similarity scores for pairs of terms, and selects a representative term from each cluster. The given information processor determines similarity scores between a secured query term received from the query processor and secured representative terms received from the given data processor, where the secured terms are based on the permutation, and the given data processor filters, without knowledge of the query term, the candidate terms of the plurality of terms based on the determined similarity scores.

Abstract: Dynamically updating a ridge regression data model of a continuous stream of data is disclosed. New data chunks corresponding to a current data accumulation point are received and the data values in the new data chunks are transformed via standardization methods. A ridge estimator for the standardized data that includes data chunks received up to a penultimate data accumulation point to include the new data chunks is dynamically updated. The cumulative observations received up to the current data accumulation point are updated and stored. Predictions for the continuous data stream are generated based on the updated ridge estimator.

Abstract: Examples disclosed herein involve data stream analytics. In examples herein, a data stream may be analyzed by computing a set of hashes of a real-valued vector, the real-valued vector corresponding to a sample data object of a data stream; generating a list of data objects from a database corresponding to the sample data object based on the set of hashes, the list of data objects ordered based on similarity of the data objects to the sample data object of the data stream; and updating a data structure representative of activity of the sample data object in the data stream based on the list of data objects, the data structure to provide incremental analysis corresponding to the sample data object.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method includes extracting features related to a plurality of users and a plurality of items and computing a correction parameter score for each of a plurality of user-item pair combinations. The method further includes computing a user response value for a user-item pair combination by applying a generalized linear model to the features of the user-item pair combination and using the correction parameter score for the user-item pair combination in the generalized linear model.

Abstract: Secure information retrieval is disclosed. One example is a system including an information retriever comprising a collection of nodes that receive a hash count from a first dataset, the first dataset including a first data term, and provide the hash count to a second dataset, the second dataset including a plurality of second data terms. A hash transformer transforms the data terms based on the hash count. A modifier modifies, for a given node, the transformed data terms. An evaluator evaluates, for each node, a similarity value between the first data term and each given second data term based on shared data elements between the modified first data term and a given modified second data term associated with the given second data term. The information retriever provides to the first dataset, at least one term identifier associated with a second data term.