chore: update publications from Google Scholar

Author: github-actions[bot]

publications.bib

@@ -1,5 +1,5 @@
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-% Merged static.bib + dynamic.bib on 2026-02-02T23:18:08Z
+% Merged static.bib + dynamic.bib on 2026-02-03T23:26:30Z
 
 @inproceedings{10.1007/978-3-032-04617-8_3,
   abstract = {Historical maps contain valuable, detailed survey data often unavailable elsewhere. Automatically extracting linear objects, such as fault lines, from scanned historical maps benefits diverse application areas, such as mining resource prediction. However, existing models encounter challenges in capturing adequate image context and spatial context. Insufficient image context leads to false detections by failing to distinguish desired linear objects from others with similar appearances. Meanwhile, insufficient spatial context hampers the accurate delineation of elongated, slender-shaped linear objects. This paper introduces the Linear Object Detection TRansformer (LDTR), which directly generates accurate vector graphs for linear objects from scanned map images. LDTR leverages multi-scale deformable attention to capture representative image context, reducing false detections. Furthermore, LDTR's innovative N-hop connectivity component explicitly encourages interactions among nodes within an N-hop neighborhood, enabling the model to learn sufficient spatial context for generating graphs with accurate connectivity. Experiments show that LDTR improves detection precision by 6{\%} and enhances line connectivity by 20{\%} over state-of-the-art baselines.},
@@ -14,7 +14,7 @@ @inproceedings{10.1007/978-3-032-04617-8_3
   isbn = {978-3-032-04617-8},
   pages = {40--59},
   publisher = {Springer Nature Switzerland},
-  title = {LDTR: Linear Object Detection Transformer for Accurate Graph Generation by Learning the N-Hop Connectivity Information},
+  title = {LDTR: Linear Object Detection Transformer for Accurate Graph Generation by Learning the N-Hop Connectivity Information},
   url = {https://link.springer.com/chapter/10.1007/978-3-032-04617-8_3},
   year = {2026}
 }
@@ -34,7 +34,7 @@ @inproceedings{10.1007/978-3-032-04617-8_4
   isbn = {978-3-032-04617-8},
   pages = {60--77},
   publisher = {Springer Nature Switzerland},
-  title = {LIGHT: Multi-modal Text Linking on Historical Maps},
+  title = {LIGHT: Multi-modal Text Linking on Historical Maps},
   url = {https://link.springer.com/chapter/10.1007/978-3-032-04617-8_4},
   year = {2026}
 }
@@ -67,7 +67,7 @@ @inproceedings{10.1007/978-3-032-09530-5_26
   isbn = {978-3-032-09530-5},
   pages = {451--471},
   publisher = {Springer Nature Switzerland},
-  title = {Exploiting LLMs and Semantic Technologies to Build a Knowledge Graph of Historical Mining Data},
+  title = {Exploiting LLMs and Semantic Technologies to Build a Knowledge Graph of Historical Mining Data},
   url = {https://link.springer.com/chapter/10.1007/978-3-032-09530-5_26},
   year = {2026}
 }
@@ -485,7 +485,7 @@ @article{10.2139/ssrn.5943954
   issn = {1556-5068},
   journal = {SSRN Electronic Journal},
   publisher = {Elsevier BV},
-  title = {<p>Validating Machine Learning–Derived Built Environment Measures From Google Street View for Urban Aging Research in India</p>},
+  title = {Validating Machine Learning–Derived Built Environment Measures From Google Street View for Urban Aging Research in India},
   url = {https://doi.org/10.2139/ssrn.5943954},
   year = {2025}
 }