Abstract: A semiconductor device comprises a first semiconductor structure, a second semiconductor structure located on the first semiconductor structure, and an active layer located between the first semiconductor structure and the second semiconductor structure. The first semiconductor structure has a first conductivity type, and includes a plurality of first layers and a plurality of second layers alternately stacked. The second semiconductor structure has a second conductivity type opposite to the first conductivity type. The plurality of first layers and the plurality of second layers include indium and phosphorus, and the plurality of first layers and the plurality of second layers respectively have a first indium atomic percentage and a second indium atomic percentage. The second indium atomic percentage is different from the first indium atomic percentage.

Abstract: Ordering search results may include obtaining an exposed log file from a log system, computing a Bayesian posterior probability for relevancy between the log file and a search request, computing an expected value of the relevancy between the log file and the search request based on the Bayesian posterior probability, storing the search request and an identifier of the log file as a key and the expected value of the relevancy between the log file and the search request as a value into a search data structure, in response to receiving a search request submitted by a user, finding expected values of relevancy between the submitted search request and log files that are relevant to the submitted search request from the search data structure, and ordering the found log files in a descending order of the expected values.

Abstract: Ordering search results may include obtaining an exposed log file from a log system, computing a Bayesian posterior probability for relevancy between the log file and a search request, computing an expected value of the relevancy between the log file and the search request based on the Bayesian posterior probability, storing the search request and an identifier of the log file as a key and the expected value of the relevancy between the log file and the search request as a value into a search data structure, in response to receiving a search request submitted by a user, finding expected values of relevancy between the submitted search request and log files that are relevant to the submitted search request from the search data structure, and ordering the found log files in a descending order of the expected values.

Abstract: Ordering search results may include obtaining an exposed log file from a log system, computing a Bayesian posterior probability for relevancy between the log file and a search request, computing an expected value of the relevancy between the log file and the search request based on the Bayesian posterior probability, storing the search request and an identifier of the log file as a key and the expected value of the relevancy between the log file and the search request as a value into a search data structure, in response to receiving a search request submitted by a user, finding expected values of relevancy between the submitted search request and log files that are relevant to the submitted search request from the search data structure, and ordering the found log files in a descending order of the expected values.

Abstract: The present disclosure provides a search ranking method and apparatus based on a click through rate (CTR) to improve reusability and simplify a ranking process. Before a search ranking, click data of a user within a preset period of time is obtained and a respective weight of each characteristic is determined based on the click data. The search ranking may include the following operations. A query and one or more query targets matching the query are obtained. A respective characteristic of each of the query and the query targets are extracted. With respect to each query target, based on the characteristics of the query and the query targets as well as the respective weight corresponding to each characteristic, a respective CTR is obtained based on one or more models such as a regression model. The query targets are ranked based on their respective CTR and displayed to the user.

Abstract: Ordering search results may include obtaining an exposed log file from a log system, computing a Bayesian posterior probability for relevancy between the log file and a search request, computing an expected value of the relevancy between the log file and the search request based on the Bayesian posterior probability, storing the search request and an identifier of the log file as a key and the expected value of the relevancy between the log file and the search request as a value into a search data structure, in response to receiving a search request submitted by a user, finding expected values of relevancy between the submitted search request and log files that are relevant to the submitted search request from the search data structure, and ordering the found log files in a descending order of the expected values.

Abstract: Ordering search results may include obtaining an exposed log file from a log system, computing a Bayesian posterior probability for relevancy between the log file and a search request, computing an expected value of the relevancy between the log file and the search request based on the Bayesian posterior probability, storing the search request and an identifier of the log file as a key and the expected value of the relevancy between the log file and the search request as a value into a search data structure, in response to receiving a search request submitted by a user, finding expected values of relevancy between the submitted search request and log files that are relevant to the submitted search request from the search data structure, and ordering the found log files in a descending order of the expected values.

Abstract: Ordering search results may include obtaining an exposed log file from a log system, computing a Bayesian posterior probability for relevancy between the log file and a search request, computing an expected value of the relevancy between the log file and the search request based on the Bayesian posterior probability, storing the search request and an identifier of the log file as a key and the expected value of the relevancy between the log file and the search request as a value into a search data structure, in response to receiving a search request submitted by a user, finding expected values of relevancy between the submitted search request and log files that are relevant to the submitted search request from the search data structure, and ordering the found log files in a descending order of the expected values.

Abstract: The present disclosure discloses a method of providing suggested terms. The method includes: receiving an initial query input from a user, and obtaining corresponding suggested queries based on the initial query; determining at least two categories corresponding to the suggested queries and at least two clickable regions usable for looking up the suggested queries; separately determining a category weight associated with each obtained category in each clickable region for the suggested queries, and a click attribute weight associated with each clickable region; computing a degree of confidence of each category for the suggested queries; and separately determining target categories for the suggested queries based on the degree of confidence of each category for the suggested queries. As such, the user may quickly identify his/her search intention based on the target categories corresponding to the suggested queries, thereby effectively improving the speed of information searching.

Abstract: Techniques and technologies are provided for predicting when remaining storage capacity of a memory component will be fully consumed. For example, the remaining storage capacity of the memory component can be determined and a rate of change of the storage capacity can be calculated. Using this information, a prediction or estimate can be made as to when the remaining storage capacity of the memory component will be fully consumed. The prediction or estimate can be based, for example, on a ratio of the remaining storage capacity to the rate of change of the storage capacity.

Abstract: A parallel aggregation system implemented in a database programming language that utilizes servers that comprise a heterogeneous server farm. The system employs a central job coordinator that coordinates multiple jobs on many disparately sized server machines. The central job coordinator further assigns jobs to the server machines based on the ability of the machine to undertake the work and the complexity and size of the particular job, and monitors the health associated with the machines that comprise the heterogeneous server farm.

Abstract: Techniques and technologies are provided for predicting when remaining storage capacity of a memory component will be fully consumed. For example, the remaining storage capacity of the memory component can be determined and a rate of change of the storage capacity can be calculated. Using this information, a prediction or estimate can be made as to when the remaining storage capacity of the memory component will be fully consumed. The prediction or estimate can be based, for example, on a ratio of the remaining storage capacity to the rate of change of the storage capacity.