add chapter sorting mechanism

content/dl4traj/_index.md

@@ -1,10 +1,11 @@
 +++
 title = "Deep Learning for Trajectory"
-sort_by = "date"
+sort_by = "none"
 paginate_by = 10
 
 [extra]
 abbr = "DL4traj"
+use_chapter_sort = true
 +++
 
 Deep learning approaches for analysis and utilization of spatiotemporal trajectories.

content/dl4traj/end-to-end/index.md

@@ -2,6 +2,9 @@
 title = "End-to-end Learning of Trajectories"
 date = 2026-02-02
 description = ""
+
+[extra]
+chapter = "2"
 +++
 
 End-to-end learning means training a model to perform a task from input to output, supervising only on how the output aligns with the task's ground truth.

content/dl4traj/generation/index.md

@@ -2,6 +2,9 @@
 title = "Trajectory Generation"
 date = 2026-02-04
 description = ""
+
+[extra]
+chapter = "4"
 +++
 
 Trajectory generation, or trajectory synthesis, aims to generate trajectories that are not actually recorded in the available trajectory data, but are still realistic and follow a target distribution.

content/dl4traj/introduction/index.md

@@ -2,6 +2,9 @@
 title = "Introduction to Deep Learning for Trajectories"
 date = 2026-02-01
 description = ""
+
+[extra]
+chapter = "1"
 +++
 
 A spatiotemporal trajectory is a sequence, with each item being a timestamped location. It records the movement of an object or a human through time and space.

content/dl4traj/self-supervised/index.md

@@ -2,6 +2,9 @@
 title = "Self-supervised Learning of Trajectories"
 date = 2026-02-03
 description = ""
+
+[extra]
+chapter = "3"
 +++
 
 Self-supervised learning means training a model with unlabeled data, using supervisory signals extracted from the data itself. It usually does not set the model to perform a certain task, but aims to learn task-agnostic information in the data.