publication selection buttons

Author: zkingston

_bibliography/papers.bib

@@ -282,7 +282,7 @@ @inproceedings{guo2025castl
   abbr         = {ICRA},
   preview      = {castl.jpg}
 }
-@inproceedings{buynitsky2025wksp,
+@misc{buynitsky2025wksp,
   title        = {Faster Behavior Cloning with Hardware-Accelerated Motion Planning},
   author       = {Alexiy Buynitsky and Zachary Kingston},
   booktitle    = {IEEE ICRA 2025 Workshop---RoboARCH: Robotics Acceleration with Computing Hardware and Systems},
@@ -292,19 +292,6 @@ @inproceedings{buynitsky2025wksp
   abbr         = {WKSP},
   preview      = {fast_bc.jpg}
 }
-@misc{coumar2025ascii,
-  title        = {Evaluating Machine Learning Approaches for {ASCII} Art Generation},
-  author       = {Sai Coumar and Zachary Kingston},
-  abstract     = {Generating structured ASCII art using computational techniques demands a careful interplay between aesthetic representation and computational precision, requiring models that can effectively translate visual information into symbolic text characters. Although Convolutional Neural Networks (CNNs) have shown promise in this domain, the comparative performance of deep learning architectures and classical machine learning methods remains unexplored. This paper explores the application of contemporary ML and DL methods to generate structured ASCII art, focusing on three key criteria: fidelity, character classification accuracy, and output quality. We investigate deep learning architectures, including Multilayer Perceptrons (MLPs), ResNet, and MobileNetV2, alongside classical approaches such as Random Forests, Support Vector Machines (SVMs) and k-Nearest Neighbors (k-NN), trained on an augmented synthetic dataset of ASCII characters. Our results show that complex neural network architectures often fall short in producing high-quality ASCII art, whereas classical machine learning classifiers, despite their simplicity, achieve performance similar to CNNs. Our findings highlight the strength of classical methods in bridging model simplicity with output quality, offering new insights into ASCII art synthesis and machine learning on image data with low dimensionality.},
-  eprint       = {2503.14375},
-  archiveprefix = {arXiv},
-  primaryclass = {cs.GR},
-  year         = 2025,
-  pdf          = {https://arxiv.org/abs/2503.14375},
-  code         = {https://github.com/saiccoumar/deep_ascii_converter},
-  abbr         = {ARXIV},
-  preview      = {ascii.jpg}
-}
 @inproceedings{liang2024ropras,
   title        = {Scaling Long-Horizon Online {POMDP} Planning via Rapid State Space Sampling},
   author       = {Yuanchu Liang* and Edward Kim* and Wil Thomason* and Zachary Kingston* and Hanna Kurniawati and Lydia E. Kavraki},
@@ -316,7 +303,7 @@ @inproceedings{liang2024ropras
   abbr         = {ISRR},
   preview      = {ropras_1.jpg}
 }
-@inproceedings{meng2024icra40,
+@misc{meng2024icra40,
   title        = {Perception-aware Planning for Robotics: Challenges and Opportunities},
   author       = {Qingxi Meng and Carlos Quintero-Peña and Zachary Kingston and Vaibhav Unhelkar and Lydia E. Kavraki},
   abstract     = {In this work, we argue that new methods are needed to generate robot motion for navigation or manipulation while effectively achieving perception goals. We support our argument by conducting experiments with a simulated robot that must accomplish a primary task, such as manipulation or navigation, while concurrently monitoring an object in the environment. Our preliminary study demonstrates that a decoupled approach fails to achieve high success in either action-focused motion generation or perception goals, motivating further developments of approaches that holistically consider both goals.},

_pages/publications.md

@@ -41,8 +41,8 @@ nav_order: 2
 
 <div class="filter-buttons">
   <button class="filter-btn active" data-filter="all">All</button>
-  <button class="filter-btn" data-filter="article">Journal Articles</button>
-  <button class="filter-btn" data-filter="inproceedings">Conference Papers</button>
+  <button class="filter-btn" data-filter="article">Journals</button>
+  <button class="filter-btn" data-filter="inproceedings">Conferences</button>
   <button class="filter-btn" data-filter="misc">Preprints</button>
 </div>
 
@@ -55,16 +55,13 @@ document.addEventListener('DOMContentLoaded', function() {
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   const publicationEntries = document.querySelectorAll('.bibliography li');
 
-  // Extract entry type from BibTeX code block
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     const bibtexDiv = entry.querySelector('.bibtex code');
     if (bibtexDiv) {
       const bibtexText = bibtexDiv.textContent;
-      // Extract the entry type (e.g., @article, @inproceedings, @misc)
       const typeMatch = bibtexText.match(/@(\w+)\s*\{/);
       if (typeMatch) {
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-        // Map entry types to filter categories
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         } else if (entryType === 'inproceedings' || entryType === 'incollection' || entryType === 'inbook') {
@@ -78,18 +75,38 @@ document.addEventListener('DOMContentLoaded', function() {
     }
   });
 
-  // Handle filter button clicks
+  function updateYearSections() {
+    const yearHeaders = document.querySelectorAll('.publications h2.bibliography');
+    yearHeaders.forEach(function(yearHeader) {
+      let nextElement = yearHeader.nextElementSibling;
+      let hasVisiblePublications = false;
+
+      if (nextElement && nextElement.tagName === 'OL' && nextElement.classList.contains('bibliography')) {
+        const items = nextElement.querySelectorAll('li');
+        items.forEach(function(item) {
+          if (item.style.display !== 'none') {
+            hasVisiblePublications = true;
+          }
+        });
+      }
+
+      if (hasVisiblePublications) {
+        yearHeader.style.display = '';
+      } else {
+        yearHeader.style.display = 'none';
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+    });
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+
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         if (filter === 'all' || entryType === filter) {
@@ -98,7 +115,11 @@ document.addEventListener('DOMContentLoaded', function() {
           entry.style.display = 'none';
         }
       });
+
+      updateYearSections();
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+
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