update APPLIED run index

applied/_index/APPLIED_RUNS_INDEX.json

@@ -1,19 +1,158 @@
 [
   {
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-    "tag": "baseline_reference",
-    "script": "train_ssvep_event_snn.py",
-    "hf_folder": "applied/original",
-    "phase": 1,
+    "key": "opt01_ttfs_dta/dta_rate_0.02",
+    "tag": "dta_rate_0.02",
+    "script": "opt01_ttfs_dta.py",
+    "hf_folder": "applied/opt01_ttfs_dta",
+    "phase": 3,
     "status": "ok",
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-    "optimization_summary": null,
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+    "optimization_summary": {
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+      "research_section": "2. Temporal-Dimension Reduction (TTFS coding / DTA-TTFS)",
+      "reference": "Software-Level Optimizations for Spiking Neural Network Inference on ARM Architectures: A Deep-Dive into the Jetson Nano Ecosystem (research.md SS2)",
+      "script": "opt01_ttfs_dta.py",
+      "baseline_mode": false,
+      "method": "harmonic_matched",
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+      "efficiency": {
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+      "optimization_specific": {
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+        "dta_thr_mean": 10.0,
+        "base_thresh": 10.0
+      },
+      "config": {
+        "method": "harmonic_matched",
+        "data_root": "/kaggle/working/data",
+        "subjects": "",
+        "blocks": "",
+        "limit": 0,
+        "drop_rest": true,
+        "resplit": "block",
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+        "class_weight": false,
+        "epochs": 60,
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+        "dropout": 0.3,
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+        "time_jitter": 2,
+        "label_smoothing": 0.05,
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+        "binary_input": false,
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+        "fake_quant": 0,
+        "amp": false,
+        "spike_reg": 0.0,
+        "event_eval": true,
+        "decision_margin": 2.0,
+        "export": false,
+        "seed": 1234,
+        "hub_dir": "/kaggle/working/hub/opt01_ttfs_dta/dta_rate_0.02",
+        "repo_id": "UWU-R-13/SSVEP-SNN",
+        "push_hf": false,
+        "keep_epoch_weights": 1,
+        "no_resume": true,
+        "selftest": false,
+        "baseline": false,
+        "bench": true,
+        "bench_warmup": 10,
+        "bench_iters": 50,
+        "bench_train_steps": 20,
+        "ttfs": true,
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+        "dta_windup_patience": 200,
+        "sampling_rate": 250.0
+      }
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 ]