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Xerces2 provides high performance, fully compliant XML parsers in the Apache Xerces family. This new version of Xerces continues to build upon the Xerces Native Interface (XNI), a complete framework for building parser components and configurations that is extremely modular and easy to program. The Apache Xerces2 parser is the reference implementation of XNI but other parser components, configurations, and parsers can be written using the Xerces Native Interface. For complete design and implementation documents, refer to the XNI Manual. Xerces2 provides fully conforming XML Schema 1.0 and 1.1 processors. An experimental implementation of the "XML Schema Definition Language (XSD): Component Designators (SCD) Candidate Recommendation (January 2010)" is also provided for evaluation. For more information, refer to the XML Schema page. Xerces2 also provides a complete implementation of the Document Object Model Level 3 Core and Load/Save W3C Recommendations and provides a complete implementation of the XML Inclusions (XInclude) W3C Recommendation. It also provides support for OASIS XML Catalogs v1.1. Xerces2 is able to parse documents written according to the XML 1.1 Recommendation, except that it does not yet provide an option to enable normalization checking as described in section 2.13 of this specification. It also handles namespaces according to the XML Namespaces 1.1 Recommendation, and will correctly serialize XML 1.1 documents if the DOM level 3 load/save APIs are in use.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extensible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

Xerces2 is the next generation of high performance, fully compliant XML parsers in the Apache Xerces family. This new version of Xerces introduces the Xerces Native Interface (XNI), a complete framework for building parser components and configurations that is extremely modular and easy to program. The Apache Xerces2 parser is the reference implementation of XNI but other parser components, configurations, and parsers can be written using the Xerces Native Interface. For complete design and implementation documents, refer to the XNI Manual. Xerces2 is a fully conforming XML Schema 1.0 processor. A partial experimental implementation of the XML Schema 1.1 Structures and Datatypes Working Drafts (December 2009) and an experimental implementation of the XML Schema Definition Language (XSD): Component Designators (SCD) Candidate Recommendation (January 2010) are provided for evaluation. For more information, refer to the XML Schema page. Xerces2 also provides a complete implementation of the Document Object Model Level 3 Core and Load/Save W3C Recommendations and provides a complete implementation of the XML Inclusions (XInclude) W3C Recommendation. It also provides support for OASIS XML Catalogs v1.1. Xerces2 is able to parse documents written according to the XML 1.1 Recommendation, except that it does not yet provide an option to enable normalization checking as described in section 2.13 of this specification. It also handles namespaces according to the XML Namespaces 1.1 Recommendation, and will correctly serialize XML 1.1 documents if the DOM level 3 load/save APIs are in use.

This plugin provides support for textlint. <h3>What&rsquo;s the textlint?</h3> <p>See <a href="https://github.com/textlint/textlint">https://github.com/textlint/textlint</a></p> <h3>Usage</h3> <h4>Install textlint and rules</h4> <p>Of course, it assumes that nodejs and npm are installed.</p> <p>e.g.</p> <pre><code>$ mkdir txtlint $ cd txtlint $ npm init $ npm install textlint --save-dev $ npm install textlint-rule-max-ten textlint-rule-spellcheck-tech-word textlint-rule-no-mix-dearu-desumasu --save-dev </code></pre> <h4>Create .textlintrc</h4> <pre><code>$ touch .textlintrc </code></pre> <pre><code class="json">{ &quot;rules&quot;: { &quot;max-ten&quot;: { &quot;max&quot;: 3 }, &quot;spellcheck-tech-word&quot;: true, &quot;no-mix-dearu-desumasu&quot;: true } } </code></pre> <p>You can also set parameters to Options (see below).</p> <h4>Set textlint and .textlintrc paths</h4> <p>Set paths to the Options (see below).</p> <p>e.g.</p> <ul> <li>textlint Path: /path/to/txtlint/node_modules/.bin/textlint (textlint.cmd in Windows)</li> <li>.textlintrc Path: /path/to/textlint/.textlintrc</li> </ul> <h4>Open Action Items window</h4> <ul> <li>Click Window > Action Items.</li> <li>Click &ldquo;Show action items for currently edited file only&rdquo; icon.</li> <li>Open your markdown or text file.</li> </ul> <h3>Options</h3> <p>Tools > Options > Editor > textlint</p> <ul> <li>textlint Path: Absolute path to textlint</li> <li>.textlintrc Path: Absolute path to .textlintrc</li> <li>Options : You can set options for the textlint command</li> <li>Enable in HTML files: To use the html plugin, you can check this</li> <li>Refresh on Save: To scan the document on save, you can check this (Checked by default)</li> <li>Show Annotations: To show annotations in the glyph gutter, you can check this (Checked by default)</li> </ul> <h3>Actions</h3> <h4>Fix</h4> <p>You have to save your file before you run this action.<br/> If there is a fixable rule&rsquo;s error, you can fix it. Right-click an item > Click <code>Fix</code>.<br/> To refresh items, your document is saved once.</p> <h4>Fix All</h4> <p>You have to save your file before you run this action.<br/> If there are fixable rule&rsquo;s errors, you can fix them. Right-click an item > Click <code>Fix All</code>.<br/> This action runs <code>textlint --fix</code> command.</p> <h4>Refresh</h4> <p>You can refresh results forcibly by the following action: Right-click your editor > Click "textlint Refresh".</p> <p>You can also set the shortcut key(Tools > Options > Keymap). </p> <h3>NOTE</h3> <ul> <li>The plugin scans only current file.</li> <li>The plugin does not refresh results automatically. Please save your file or run the refresh action.</li> <li>Use <code>UTF-8</code> as file encoding and <code>LF</code> as line endings.</li> <li>This plugin may not work properly in Windows. (Please try to check above.)</li> <li>If you cannot get expected results, just try to run the <code>textlint</code> commands once in your CLI.</li> </ul>

Jadex Rules is a small lightweight rule engine, which currently employs the well-known Rete algorithm for highly efficient rule matching. Jadex rules is therefore similar to other rule engines like JESS and Drools. Despite the similarities there are also important differences between these systems: * Jadex Rules is very small and intended to be used as component of other software. Even though rules can be specified in a Java dialect as well as (a small variation of) the CLIPS language its primary usage is on the API level. Jadex Rules is currently the core component of the Jadex BDI reasoning engine. * Jadex Rules cleanly separates between state and rule representation. This allows the state implementation as well as the matcher to be flexibly exchanged. Some experiments have e.g. been conducted with a Jena representation. Regarding the matcher, it is planned to support also the Treat algorithm, which has a lower memory footprint than Rete. * Jadex Rules pays close attention to rule debugging. The state as well as the rete engine can be observed at runtime. The rule debugger provides functionalities to execute a rule program stepwise and also use rule breakpoints to stop the execution at those points.

MassBank of America (MoNA), is an auto curating repository for storing, comparing and querying mass spectra of chemical compounds. It is metadata centric and it was designed to allow easy integration into other tools by utilize its REST based application programming interface. At the current time it contains over 200k predicted and 40k unique experimental mass spectra and their associated metadata. The predicted spectra were obtained by utilizing the lipid blast library and the experimental spectra were acquired from 30 different facilities all over the world, including several major research facilities in the United States and Japan. MoNA is utilizing the InChI Key as unique identifier for chemicals and is designed for easy scalability and expendability. This is realized by utilizing common applications like nginx, grails, AngularJS, postgresSQL and tomcat. MoNA is currently integrated in applications like MSDial, BinBase, MZMine and the statistics package R. This was accomplished by utilizing its REST based API, which is also utilized by its main AngularJS based web interface. We consider MoNA to be highly useful for crosslinking mass spectra in publications, identification of unknowns and integration in data acquisition software. This package provides you with access to the REST api of the main MoNA installation.

This is an implementation of the Neural Gas algorithm on distance data (Relational Neural Gas) for unsupervised clustering. We recommend that you use the functions provided by the RelationalNeuralGas class for your purposes. All other classes and functions are utilities which are used by this central class. In particular, you can use RelationalNeuralGas.train() to obtain a RNGModel (i.e. a clustering of your data), and subsequently you can use RelationalNeuralGas.getAssignments() to obtain the resulting cluster assignments, and RelationalNeuralGas.classify() to cluster new points which are not part of the training data set. The underlying scientific work is summarized nicely in the dissertation "Topographic Mapping of Dissimilarity Datasets" by Alexander Hasenfuss (2009). The basic properties of an Relational Neural Gas algorithm are the following: 1.) It is relational: The data is represented only in terms of a pairwise distance matrix. 2.) It is a clustering method: The algorithm provides a clustering model, that is: After calculation, each data point should be assigned to a cluster (for this package here we only consider hard clustering, that is: each data point is assigned to exactly one cluster). 3.) It is a vector quantization method: Each cluster corresponds to a prototype, which is in the center of the cluster and data points are assigned to the cluster if and only if they are closest to this particular prototype. 4.) It is rank-based: The updates of the prototypes depend only on the distance ranking, not on the absolute value of the distances.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

Includes 2 main parts: - Path finding through 2D polygons using the A star algorithm and navigation-mesh generation Field of vision / shadows / line of sight / lighting. The basic polygon and point classes are the KPolygon and KPoint. KPolygon contains a list of KPoints for vertices as well as a center (centroid), area, and radius (circular bound or distance from center to furthest point). KPolygon was born out of the need for a more game-oriented and flexible polygon class than the Path2D class in the standard Java library. KPolygon implements java.awt.geom.Shape so it can be easily drawn and filled by Java2D's Graphics2D object. - This API provides path-finding and field-of-vision. For other complex geometric operations such as buffering (fattening and shrinking) and constructive area geometry (intersections and unions) it is recommended to use the excellent Java Topology Suite (JTS). The standard Java2D library also provides the Area class which can be used for some constructive area geometry operations. Note that there is a utility class PolygonConverter that can quickly convert KPolygons to JTS polygons and vice versa.