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EnOS Device And Asset API pojo 0.2.14-SNAPSHOT ota api pojo and event 0.2.20-SNAPSHOT The Response objects returned by GetEvent and SearchEvent queries are added (including: outputData, eventName, outputNames fields) 0.2.21-SNAPSHOT Increase the internationalization of the four-element description field of the model 0.2.22-SNAPSHOT add some pojo class for private api 0.2.23-SNAPSHOT model-service open api的接口中thingModel增加category模型类型字段 0.2.24-SNAPSHOT 部分search接口增加realTime字段 0.2.25-SNAPSHOT firmware add tag function 0.2.27-SNAPSHOT add max and min fields 0.2.28-SNAPSHOT asset tree api for amc/city infra 0.2.29-SNAPSHOT fix thingModel enableMeasurepointValidate 0.2.30-SNAPSHOT enhancement of search model and update asset and alert engine 增加告警规则sdk 0.2.31-SNAPSHOT move asset node 2.4.0-SNAPSHOT upgrade to 2.4 for support DCM 2.4GA 2.4.1-SNAPSHOT service parameter supports "Required" and "Default Value" fields 2.4.2-SNAPSHOT upgrade to 2.4 for support DCM 2.4GA jar 升级 2.4.3-SNAPSHOT add alert-engine v2.5 alertRule openAPI

Closure Compiler is a JavaScript optimizing compiler. It parses your JavaScript, analyzes it, removes dead code and rewrites and minimizes what's left. It also checks syntax, variable references, and types, and warns about common JavaScript pitfalls. It is used in many of Google's JavaScript apps, including Gmail, Google Web Search, Google Maps, and Google Docs. This distribution is slightly modified from the original for easier use with J2CL outside of Bazel by Vertispan LLC. The links and references in this pom.xml will reference the Vertispan fork, to ensure that any error introduced by our changes are not incorrectly blamed on the upstream Google repository.

This package contains several example programs of the usage of the Chips-n-Salsa library. Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. Chips-n-Salsa's source code is maintained on GitHub, and the prebuilt jars of the library can be imported from Maven Central using maven or other build tools. The purpose of the package chips-n-salsa-examples is to demonstrate usage of the major functionality of the Chips-n-Salsa library. You can find out more about the Chips-n-Salsa library itself from its website: https://chips-n-salsa.cicirello.org/.

# heatshrink-java Java library used to encode/decode [heatshrink](https://github.com/atomicobject/heatshrink) compressed data. ## Building ```bash ./gradlew build ``` ## Gradle dependency See https://search.maven.org/artifact/io.github.markrileybot/heatshrink-java/ ## Usage ### Java library ```java int windowSize = 9; int lookaheadSize = 8; ByteArrayOutputStream baos = new ByteArrayOutputStream(); try(HsOutputStream out = new HsOutputStream(baos, windowSize, lookaheadSize)) { out.write("ABCABCABCABCABCABC".getBytes()); } try(HsInputStream hsi = new HsInputStream(new ByteArrayInutStream(baos.toByteArray()), windowSize, lookaheadSize)) { byte[] res = new byte[512]; int len = hsi.read(res); System.out.println(new String(res, 0, len)); } ``` ### CLI ```bash $ java -jar heatshrink-java-exe.jar [-h] [-e|-d] [-v] [-w SIZE] [-l BITS] [IN_FILE] [OUT_FILE] ``` ## Status [](https://github.com/markrileybot/heatshrink-java/actions/workflows/gradle-publish.yml) [](https://coveralls.io/github/markrileybot/heatshrink-java?branch=master) [](https://maven-badges.herokuapp.com/maven-central/io.github.markrileybot/geokey)

Concourse is a distributed database warehouse for transactions, search, and analytics across time. Developers use Concourse to quickly build mission critical software while also benefiting from real time insight into their most important data. With Concourse, end-to-end data management requires no extra infrastructure, no prior configuration and no additional coding–all of which greatly reduce costs and allow developers to focus on core business problems.

This project contains a JNA (Java Native Access) implementation built on top of the native Hnswlib (Hierarchical Navigable Small World Graph) which offers a fast approximate nearest neighbor search. It includes some modifications and simplifications in order to provide Hnswlib features with native like performance to applications written in Java. Differently from the original Python implementation, the multi-thread support is not included in the bindings itself but it can be easily implemented on the Java side.