Abstract: A power amplifier device includes an amplification path implemented within a power amplifier package. The amplification path includes input and output package leads, a transistor die with transistor input and output terminals and a power transistor, and a two-stage input impedance matching circuit electrically coupled between the input package lead and the transistor input terminal. The two-stage input impedance matching circuit has a double T-match topology that includes a first resonator coupled to the first input package lead, and a second resonator coupled between the first resonator and the transistor input terminal. The amplification path also includes an output impedance matching circuit coupled between the transistor output terminal and the first output package lead, and a second output harmonic termination circuit coupled to the first output package lead.

Abstract: Disclosed herein are a base having a storage function and a basketball stand comprising the base. The base includes a base housing internally provided with a cavity, wherein an upper portion of the base housing is recessed inwards to form a hemispheric basketball storage recess which is used for storing a basketball; and the bottom of the base housing is bilaterally symmetrically recessed inwards to form support frame storage grooves which are used for storing support frames. The basketball stand includes the base. The basketball is stored in the basketball storage recess, and the support frames are stored in the support frame storage grooves, thereby facilitating storage, avoiding losing of parts after dismounting, and facilitating usage.

Abstract: An amplifier device, such as a Doherty amplifier device, may include an extra lead and decoupling capacitor coupled to radio frequency (RF) cold points of output impedance matching circuitry of multiple amplification paths, such as a carrier path and peaking path, of the amplifier device. The extra lead and the decoupling capacitor are configured to provide low frequency resonance decoupling for the multiple amplification paths. A drain bias voltage may be provided to the drain terminals of transistors of amplifiers of the amplifier device via the extra lead. An integrated passive device (IPD) including a wire fence and one or more conductive pads may be disposed between a carrier amplifier die and a peaking amplifier. The extra lead may be coupled to the RF cold points via the IPD. The wire fence may mitigate RF interference between the carrier amplifier die and the peaking amplifier die.

Abstract: Disclosed is a liftable basketball stand, including a base, a backboard and a basket and further including a liftable support frame. A bottom of the liftable support frame is fixedly connected to the base, a top of the liftable support frame is fixedly connected to the backboard, and the basket is fixed to the backboard. The liftable support frame at least includes a first main support rod, a second main support rod and a connecting piece, wherein the first main support rod is sleeved inside the second main support rod, is movably connected to the second main support rod through the connecting piece and is capable of moving upwards or downwards relative to the second main support rod. The basketball stand disclosed by the present invention can be adjusted arbitrarily at different heights, so that the basketball stand features simple adjustment mode and high stability.

Abstract: Copper-tin-nickel brazing material prepared by alloys recycled from E-waste, preparation method therefor, and system thereof are provided. A preparation method for the copper-tin-nickel brazing material includes the following steps: (a) spreading nano-SiO2 on the bottom of crucible and then adding a crude copper-tin-iron-nickel alloy recycled from E-waste; (b) heating the crucible to melt the crude alloy into a metal liquid so that Zn and Pb in the metal liquid react with the SiO2 to form a slag that floats out; (c) introducing a refining gas to the bottom of metal liquid in step (b), thereby removing the scums or gases formed by Pb, Fe, S, and O in the metal liquid; (d) performing heat-preserving directional solidification on the metal liquid, to bias-aggregate the Fe and Sb at one end and remove the same to obtain a copper-based intermediate alloy; and smelting and powdering the copper-based intermediate alloy.

Abstract: A machine learning model can be trained to infer the probability of the presence of categories of a software bug in a source code file. A bug tracker can provide information concerning the category to which a software bug belongs. The bug data supplied to a machine learning model for inferring the presence of particular categories of bugs can be filtered to exclude a specified category or categories of bugs. Information including but not limited to organizational boundaries can be inferred from the category of bugs present in a body of source code. The inferred organization boundaries can be used to generate team-specific machine learning models.

Abstract: A data copy method comprises: acquiring, by a destination server, a data block identifier, version information and hole information; transmitting, by the destination server, the data block identifier, the version information and the hole information to a source terminal server so as to enable the source server to find corresponding data according to the data block identifier, the version information and the hole information; receiving, by the destination server, a response message carrying the data transmitted by the source server and copying the data to a hole corresponding to the data block identifier, the version information and the hole according to the response message.

Abstract: Data storage method, apparatus, and system are disclosed. The data storage method includes: encoding a data block using an erasure coding to obtain corresponding multiple data fragments; separately writing the multiple data fragments to corresponding allocated storage nodes; and determining that writing of the data block is successful if a number of storage nodes to which data fragments is successfully written within a first defined time duration is greater than or equal to a defined value, wherein the defined value is greater than or equal to a number of data block fragments in the multiple data fragments and is less than or equal to a number of the multiple data fragments. As such, the latency of a data write can be reduced, and the performance stability can be improved when a storage node encounters a failure.

Abstract: Embodiments of the disclosure provide a data write-in method and apparatus. The data write-in method includes: selecting a target replica server from a plurality of replica servers managed by a metadata server according to remaining capacity of the plurality of replica servers; selecting a write-in disk in the target replica server according to remaining capacity and load of disks managed by the target replica server; and storing write-in data into the write-in disk through the target replica server.

Abstract: Data storage method, apparatus, and system are disclosed. The data storage method includes: encoding a data block using an erasure coding to obtain corresponding multiple data fragments; separately writing the multiple data fragments to corresponding allocated storage nodes; and determining that writing of the data block is successful if a number of storage nodes to which data fragments is successfully written within a first defined time duration is greater than or equal to a defined value, wherein the defined value is greater than or equal to a number of data block fragments in the multiple data fragments and is less than or equal to a number of the multiple data fragments. As such, the latency of a data write can be reduced, and the performance stability can be improved when a storage node encounters a failure.

Abstract: A method for updating data in a distributed storage system is disclosed. The method may comprise receiving, by a sender terminal device, an update request; obtaining, by the sender terminal device, update data and control information from the update request; sending, by the sender terminal device, the update data, via a data update request, to a receiver terminal device; and sending, by the sender terminal device, the control information, via a control information update request, to the receiver terminal device.

Abstract: Information concerning software bugs including bug detection, bug prediction data and/or historical bug data can be used to determine whether it is safe to commit, integrate, deploy and/or deliver a software change. If the change is deemed unsafe, the change can be rejected automatically. Alternatively, the change can proceed following approval by an administrator, supervisor, implementer, manager and/or other designated approval mechanism. Actions taken to override a block can be recorded along with information concerning failed deployments and/or deliveries, a higher than normal customer failure rate after deployment and/or delivery or through manual data entry.

Abstract: Information concerning software bugs including bug detection, bug prediction data and/or historical bug data can be used to determine whether it is safe to commit, integrate, deploy and/or deliver a software change. If the change is deemed unsafe, the change can be rejected automatically. Alternatively, the change can proceed following approval by an administrator, supervisor, implementer, manager and/or other designated approval mechanism. Actions taken to override a block can be recorded along with information concerning failed deployments and/or deliveries, a higher than normal customer failure rate after deployment and/or delivery or through manual data entry.

Abstract: For each detected bug, historical code with similar characteristics and bug corrections from a historical bug dataset can be displayed in a source code editor. Relevant training and/or testing data can be found by comparing an internal representation of the code under development with an internal representation of the original buggy code in the historical bug dataset. Training and/or testing data that is relevant to the current code can be distinguished from irrelevant training and/or testing data by determining that the code syntax tokens from the current and historical data overlap to at least a specified percentage. Code can be devolved into a set of metrics. The degree of overlap between the metric sets can be determined. If a computed risk factor for the bug correction meets or exceeds a specified threshold, the bug correction can be automatically applied. Additional testing can be automatically performed on and/or added to the corrected code.

Abstract: A machine learning model can be trained to infer the probability of the presence of categories of a software bug in a source code file. A bug tracker can provide information concerning the category to which a software bug belongs. The bug data supplied to a machine learning model for inferring the presence of particular categories of bugs can be filtered to exclude a specified category or categories of bugs. Information including but not limited to organizational boundaries can be inferred from the category of bugs present in a body of source code. The inferred organization boundaries can be used to generate team-specific machine learning models.

Abstract: For each detected bug, historical code with similar characteristics and bug corrections from a historical bug dataset can be displayed in a source code editor. Relevant training and/or testing data can be found by comparing an internal representation of the code under development with an internal representation of the original buggy code in the historical bug dataset. Training and/or testing data that is relevant to the current code can be distinguished from irrelevant training and/or testing data by determining that the code syntax tokens from the current and historical data overlap to at least a specified percentage. Code can be devolved into a set of metrics. The degree of overlap between the metric sets can be determined. If a computed risk factor for the bug correction meets or exceeds a specified threshold, the bug correction can be automatically applied. Additional testing can be automatically performed on and/or added to the corrected code.

Abstract: A risk factor that software written by a developer includes bugs can be calculated for the developer. The risk factor can be used to determine the quality of the developer's code. The risk factor associated with code produced by a particular developer can be provided to a manager or management system. The risk factor can be used to provide bug-based information to a corporate review and reward process.

Abstract: Information concerning software bugs including bug detection, bug prediction data and/or historical bug data can be used to determine whether it is safe to commit, integrate, deploy and/or deliver a software change. If the change is deemed unsafe, the change can be rejected automatically. Alternatively, the change can proceed following approval by an administrator, supervisor, implementer, manager and/or other designated approval mechanism. Actions taken to override a block can be recorded along with information concerning failed deployments and/or deliveries, a higher than normal customer failure rate after deployment and/or delivery or through manual data entry.

Abstract: Embodiments of the disclosure provide a data write-in method and apparatus. The data write-in method includes: selecting a target replica server from a plurality of replica servers managed by a metadata server according to remaining capacity of the plurality of replica servers; selecting a write-in disk in the target replica server according to remaining capacity and load of disks managed by the target replica server; and storing write-in data into the write-in disk through the target replica server.

Abstract: A probabilistic machine learning model is generated to identify potential bugs in a source code file. Source code files with and without bugs are analyzed to find features indicative of a pattern of the context of a software bug, wherein the context is based on a syntactic structure of the source code. The features may be extracted from a line of source code, a method, a class and/or any combination thereof. The features are then converted into a binary representation of feature vectors that train a machine learning model to predict the likelihood of a software bug in a source code file.