diff --git a/README.md b/README.md
index bb1529eedf..8437435c8e 100644
--- a/README.md
+++ b/README.md
@@ -65,7 +65,7 @@ following to your project pom.xml file.
     <dependency>
       <groupId>com.github.haifengl</groupId>
       <artifactId>smile-core</artifactId>
-      <version>3.0.0</version>
+      <version>3.0.1</version>
     </dependency>
 ```
 
@@ -73,18 +73,18 @@ For NLP, use the artifactId smile-nlp.
 
 For Scala API, please use
 ```
-    libraryDependencies += "com.github.haifengl" %% "smile-scala" % "3.0.0"
+    libraryDependencies += "com.github.haifengl" %% "smile-scala" % "3.0.1"
 ```
 
 For Kotlin API, add the below into the `dependencies` section
 of Gradle build script.
 ```
-    implementation("com.github.haifengl:smile-kotlin:3.0.0")
+    implementation("com.github.haifengl:smile-kotlin:3.0.1")
 ```
 
 For Clojure API, add the following dependency to your project or build file:
 ```
-    [org.clojars.haifengl/smile "3.0.0"]
+    [org.clojars.haifengl/smile "3.0.1"]
 ```
 
 Some algorithms rely on BLAS and LAPACK (e.g. manifold learning,
@@ -113,7 +113,7 @@ Or you may simply include `smile-mkl` module in your project, which includes
 MKL binaries. With `smile-mkl` module in the class path, Smile will
 automatically switch to MKL.
 ```
-    libraryDependencies += "com.github.haifengl" %% "smile-mkl" % "3.0.0"
+    libraryDependencies += "com.github.haifengl" %% "smile-mkl" % "3.0.1"
 ```
 
 ## Shell
@@ -149,11 +149,7 @@ to enter Kotlin REPL.
 ## Model Serialization
 Most models support the Java `Serializable` interface (all classifiers
 do support `Serializable` interface) so that you can use them in Spark.
-For reading/writing the models in non-Java code, we suggest [XStream]
-(https://github.com/x-stream/xstream) to serialize the trained models.
-XStream is a simple library to serialize objects to XML and back again.
-XStream is easy to use and doesn't require mappings (actually requires
-no modifications to objects). [Protostuff](http://code.google.com/p/protostuff/)
+[Protostuff](http://code.google.com/p/protostuff/)
 is a nice alternative that supports forward-backward compatibility
 (schema evolution) and validation. Beyond XML, Protostuff supports many
 other formats such as JSON, YAML, protobuf, etc.
@@ -169,7 +165,7 @@ To use SmilePlot, add the following to dependencies
     <dependency>
       <groupId>com.github.haifengl</groupId>
       <artifactId>smile-plot</artifactId>
-      <version>3.0.0</version>
+      <version>3.0.1</version>
     </dependency>
 ```
 
diff --git a/web/data.html b/web/data.html
index 92aff09e2d..58a472d971 100644
--- a/web/data.html
+++ b/web/data.html
@@ -2108,24 +2108,6 @@ <h2 id="export">Export Data and Models</h2>
     <p>This method is in the Scala API <code>smile.write</code> object and serialize the model to Java
         serialization format. This is handy if you want to use a model in Spark.</p>
 
-    <p>Alternatively, you can also use</p>
-    <ul class="nav nav-tabs">
-        <li class="active"><a href="#scala_26" data-toggle="tab">Scala</a></li>
-    </ul>
-    <div class="tab-content">
-        <div class="tab-pane active" id="scala_26">
-            <div class="code" style="text-align: left;">
-    <pre class="prettyprint lang-scala"><code>
-    write.xstream(model, file)
-    </code></pre>
-            </div>
-        </div>
-    </div>
-
-    <p>which uses XStream library to serialize the model (actually any objects) to XML file.</p>
-
-    <p>To read the model back, you can use <code>read(file)</code> or <code>read.xstream(file)</code>, correspondingly.</p>
-
     <p>You can also save a <code>DataFrame</code> to an ARFF file with the method
         <code>write.arff(data, file)</code>. The ARFF file keeps the data type information.
         If you prefer the plain csv text file, you may use the methods <code>write.csv(data, file)</code> or
diff --git a/web/faq.html b/web/faq.html
index 9ff3ac0526..eac8360829 100644
--- a/web/faq.html
+++ b/web/faq.html
@@ -27,24 +27,24 @@ <h2 class="question" id="link-with-smile">Link with Smile</h2>
     &lt;dependency&gt;
       &lt;groupId&gt;com.github.haifengl&lt;/groupId&gt;
       &lt;artifactId&gt;smile-core&lt;/artifactId&gt;
-      &lt;version&gt;3.0.0&lt;/version&gt;
+      &lt;version&gt;3.0.1&lt;/version&gt;
     &lt;/dependency&gt;
     </code></pre>
 
     <p>If you're using Gradle, add the following line into your build
         file's <code>dependencies</code> section:</p>
     <pre><code>
-    implementation("com.github.haifengl:smile-core:3.0.0")
+    implementation("com.github.haifengl:smile-core:3.0.1")
     </code></pre>
 
     <p>If you're using SBT, add the following line into your build file:</p>
     <pre><code>
-    libraryDependencies += "com.github.haifengl" % "smile-core" % "3.0.0"
+    libraryDependencies += "com.github.haifengl" % "smile-core" % "3.0.1"
     </code></pre>
 
     <p>For Scala API,</p>
     <pre><code>
-    libraryDependencies += "com.github.haifengl" %% "smile-scala" % "3.0.0"
+    libraryDependencies += "com.github.haifengl" %% "smile-scala" % "3.0.1"
     </code></pre>
 
     <p>Some algorithms rely on BLAS and LAPACK (e.g. manifold learning,
@@ -74,7 +74,7 @@ <h2 class="question" id="link-with-smile">Link with Smile</h2>
         includes MKL binaries. With `smile-mkl` module in the class path, Smile
         will automatically switch to MKL.</p>
     <pre><code>
-    libraryDependencies += "com.github.haifengl" % "smile-mkl" % "3.0.0"
+    libraryDependencies += "com.github.haifengl" % "smile-mkl" % "3.0.1"
     </code></pre>
 
     <h2 class="question" id="model-serialization">Model serialization</h2>
@@ -85,15 +85,6 @@ <h2 class="question" id="model-serialization">Model serialization</h2>
     <p>This method is in the Scala API <code>smile.write</code> object and serialize the model to Java
     serialization format. This is handy if you want to use a model in Spark.</p>
 
-    <p>Alternatively, you can also use</p>
-    <pre class="prettyprint lang-scala"><code>
-    write.xstream(model, file)
-    </code></pre>
-
-    <p>which uses XStream library to serialize the model (actually any objects) to XML file.</p>
-
-    <p>To read the model back, you can use <code>read(file)</code> or <code>read.xstream(file)</code>, correspondingly.</p>
-
     <h2 class="question" id="data-format">Data Format</h2>
     <p class="answer">
         Most Smile algorithms take simple <code>double[]</code> as input. So you can use your favorite methods
diff --git a/web/index.html b/web/index.html
index 28e00295e7..a988fa9998 100644
--- a/web/index.html
+++ b/web/index.html
@@ -6,6 +6,9 @@
   <div class="news" style="margin-bottom: 20px;">
     <h5>Latest News</h5>
     <ul class="list-unstyled">
+      <li><a href="https://github.com/haifengl/smile/releases/tag/v3.0.1">Smile 3.0.1 Released!</a>
+        <span class="small">(Mar 3, 2023)</span></li>
+
       <li><a href="https://github.com/haifengl/smile/releases/tag/v3.0.0">Smile 3.0.0 Released!</a>
         <span class="small">(Dec 15, 2022)</span></li>
 
diff --git a/web/quickstart.html b/web/quickstart.html
index 887942fa3b..b033ab1e50 100644
--- a/web/quickstart.html
+++ b/web/quickstart.html
@@ -429,7 +429,7 @@ <h2 id="notebook">Notebooks</h2>
         the <code>notebooks</code> directory.</p>
 
     <pre class="prettyprint lang-scala"><code>
-    import $ivy.`com.github.haifengl::smile-scala:3.0.0`
+    import $ivy.`com.github.haifengl::smile-scala:3.0.1`
 
     import scala.language.postfixOps
     import org.apache.commons.csv.CSVFormat