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2026-09-23 23:32:30 +08:00
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deep_learning/Q1/outputs/alignment_v2/experiment.log
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[start] samples=100 folds=5 seeds=[42, 3407, 2026] device=cuda variants=v2_a,v2_b,v2_c
[fixed fold 1/5] train=80 validation=20; evaluating unchanged M1/M2
[fixed fold 2/5] train=80 validation=20; evaluating unchanged M1/M2
[fixed fold 3/5] train=80 validation=20; evaluating unchanged M1/M2
[fixed fold 4/5] train=80 validation=20; evaluating unchanged M1/M2
[fixed fold 5/5] train=80 validation=20; evaluating unchanged M1/M2
[v2_a fold 1/5] training M3/M4 with 80 train and 20 validation clips
[v2_a fold 1] M3 seed=42 best_epoch=39 val=7.7805 C_row(audio/vision)=0.802/0.969
[v2_a fold 1] M4 seed=42 best_epoch=4 val=8.5170 C_row(audio/vision)=1.000/1.000
[v2_a fold 1] M3 seed=3407 best_epoch=45 val=7.6577 C_row(audio/vision)=0.728/0.961
[v2_a fold 1] M4 seed=3407 best_epoch=4 val=8.3070 C_row(audio/vision)=1.000/1.000
[v2_a fold 1] M3 seed=2026 best_epoch=30 val=7.8561 C_row(audio/vision)=0.855/0.987
[v2_a fold 1] M4 seed=2026 best_epoch=3 val=8.5412 C_row(audio/vision)=1.000/1.000
[v2_a fold 2/5] training M3/M4 with 80 train and 20 validation clips
[v2_a fold 2] M3 seed=42 best_epoch=1 val=8.5791 C_row(audio/vision)=0.995/0.999
[v2_a fold 2] M4 seed=42 best_epoch=1 val=8.3893 C_row(audio/vision)=1.000/1.000
[v2_a fold 2] M3 seed=3407 best_epoch=29 val=8.2238 C_row(audio/vision)=0.818/0.987
[v2_a fold 2] M4 seed=3407 best_epoch=3 val=8.5151 C_row(audio/vision)=1.000/1.000
[v2_a fold 2] M3 seed=2026 best_epoch=30 val=8.0788 C_row(audio/vision)=0.793/0.988
[v2_a fold 2] M4 seed=2026 best_epoch=1 val=8.4105 C_row(audio/vision)=1.000/1.000
[v2_a fold 3/5] training M3/M4 with 80 train and 20 validation clips
[v2_a fold 3] M3 seed=42 best_epoch=26 val=8.0276 C_row(audio/vision)=0.885/0.985
[v2_a fold 3] M4 seed=42 best_epoch=31 val=8.0486 C_row(audio/vision)=0.965/0.999
[v2_a fold 3] M3 seed=3407 best_epoch=25 val=8.0664 C_row(audio/vision)=0.893/0.984
[v2_a fold 3] M4 seed=3407 best_epoch=20 val=7.6810 C_row(audio/vision)=0.996/1.000
[v2_a fold 3] M3 seed=2026 best_epoch=27 val=8.0816 C_row(audio/vision)=0.836/0.979
[v2_a fold 3] M4 seed=2026 best_epoch=3 val=8.4686 C_row(audio/vision)=1.000/1.000
[v2_a fold 4/5] training M3/M4 with 80 train and 20 validation clips
[v2_a fold 4] M3 seed=42 best_epoch=25 val=8.1088 C_row(audio/vision)=0.857/0.988
[v2_a fold 4] M4 seed=42 best_epoch=1 val=8.4480 C_row(audio/vision)=1.000/1.000
[v2_a fold 4] M3 seed=3407 best_epoch=29 val=8.0979 C_row(audio/vision)=0.843/0.976
[v2_a fold 4] M4 seed=3407 best_epoch=2 val=8.4787 C_row(audio/vision)=1.000/1.000
[v2_a fold 4] M3 seed=2026 best_epoch=31 val=8.1568 C_row(audio/vision)=0.823/0.985
[v2_a fold 4] M4 seed=2026 best_epoch=2 val=8.5234 C_row(audio/vision)=1.000/1.000
[v2_a fold 5/5] training M3/M4 with 80 train and 20 validation clips
[v2_a fold 5] M3 seed=42 best_epoch=33 val=7.9642 C_row(audio/vision)=0.851/0.973
[v2_a fold 5] M4 seed=42 best_epoch=4 val=8.4633 C_row(audio/vision)=1.000/1.000
[v2_a fold 5] M3 seed=3407 best_epoch=36 val=7.9612 C_row(audio/vision)=0.840/0.955
[v2_a fold 5] M4 seed=3407 best_epoch=50 val=7.6178 C_row(audio/vision)=0.951/0.988
[v2_a fold 5] M3 seed=2026 best_epoch=28 val=7.9423 C_row(audio/vision)=0.890/0.973
[v2_a fold 5] M4 seed=2026 best_epoch=1 val=8.3293 C_row(audio/vision)=1.000/1.000
[v2_a done] elapsed=334.7s output=/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_v2/v2_a; summary rows=48
[v2_b fold 1/5] training M3/M4 with 80 train and 20 validation clips
[v2_b fold 1] M3 seed=42 best_epoch=39 val=8.2245 C_row(audio/vision)=0.746/0.963
[v2_b fold 1] M4 seed=42 best_epoch=4 val=9.0169 C_row(audio/vision)=1.000/1.000
[v2_b fold 1] M3 seed=3407 best_epoch=45 val=8.0834 C_row(audio/vision)=0.647/0.951
[v2_b fold 1] M4 seed=3407 best_epoch=4 val=8.8069 C_row(audio/vision)=1.000/1.000
[v2_b fold 1] M3 seed=2026 best_epoch=30 val=8.2653 C_row(audio/vision)=0.791/0.984
[v2_b fold 1] M4 seed=2026 best_epoch=3 val=9.0412 C_row(audio/vision)=1.000/1.000
[v2_b fold 2/5] training M3/M4 with 80 train and 20 validation clips
[v2_b fold 2] M3 seed=42 best_epoch=1 val=9.0775 C_row(audio/vision)=0.995/0.999
[v2_b fold 2] M4 seed=42 best_epoch=1 val=8.8892 C_row(audio/vision)=1.000/1.000
[v2_b fold 2] M3 seed=3407 best_epoch=29 val=8.6745 C_row(audio/vision)=0.771/0.984
[v2_b fold 2] M4 seed=3407 best_epoch=34 val=8.4230 C_row(audio/vision)=0.872/0.999
[v2_b fold 2] M3 seed=2026 best_epoch=30 val=8.5185 C_row(audio/vision)=0.736/0.986
[v2_b fold 2] M4 seed=2026 best_epoch=1 val=8.9105 C_row(audio/vision)=1.000/1.000
[v2_b fold 3/5] training M3/M4 with 80 train and 20 validation clips
[v2_b fold 3] M3 seed=42 best_epoch=26 val=8.4752 C_row(audio/vision)=0.843/0.983
[v2_b fold 3] M4 seed=42 best_epoch=31 val=8.4967 C_row(audio/vision)=0.950/0.999
[v2_b fold 3] M3 seed=3407 best_epoch=25 val=8.5216 C_row(audio/vision)=0.856/0.982
[v2_b fold 3] M4 seed=3407 best_epoch=28 val=8.0958 C_row(audio/vision)=0.943/0.999
[v2_b fold 3] M3 seed=2026 best_epoch=27 val=8.5222 C_row(audio/vision)=0.797/0.976
[v2_b fold 3] M4 seed=2026 best_epoch=3 val=8.9686 C_row(audio/vision)=1.000/1.000
[v2_b fold 4/5] training M3/M4 with 80 train and 20 validation clips
[v2_b fold 4] M3 seed=42 best_epoch=25 val=8.5698 C_row(audio/vision)=0.814/0.986
[v2_b fold 4] M4 seed=42 best_epoch=1 val=8.9479 C_row(audio/vision)=1.000/1.000
[v2_b fold 4] M3 seed=3407 best_epoch=30 val=8.5162 C_row(audio/vision)=0.787/0.966
[v2_b fold 4] M4 seed=3407 best_epoch=2 val=8.9787 C_row(audio/vision)=1.000/1.000
[v2_b fold 4] M3 seed=2026 best_epoch=40 val=8.5877 C_row(audio/vision)=0.631/0.936
[v2_b fold 4] M4 seed=2026 best_epoch=2 val=9.0233 C_row(audio/vision)=1.000/1.000
[v2_b fold 5/5] training M3/M4 with 80 train and 20 validation clips
[v2_b fold 5] M3 seed=42 best_epoch=49 val=8.3492 C_row(audio/vision)=0.659/0.836
[v2_b fold 5] M4 seed=42 best_epoch=4 val=8.9632 C_row(audio/vision)=1.000/1.000
[v2_b fold 5] M3 seed=3407 best_epoch=42 val=8.3579 C_row(audio/vision)=0.730/0.907
[v2_b fold 5] M4 seed=3407 best_epoch=28 val=8.1570 C_row(audio/vision)=0.975/1.000
[v2_b fold 5] M3 seed=2026 best_epoch=28 val=8.3718 C_row(audio/vision)=0.847/0.969
[v2_b fold 5] M4 seed=2026 best_epoch=1 val=8.8293 C_row(audio/vision)=1.000/1.000
[v2_b done] elapsed=351.7s output=/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_v2/v2_b; summary rows=48
[v2_c fold 1/5] training M3/M4 with 80 train and 20 validation clips
[v2_c fold 1] M3 seed=42 best_epoch=39 val=8.5597 C_row(audio/vision)=0.753/0.964
[v2_c fold 1] M4 seed=42 best_epoch=4 val=9.4480 C_row(audio/vision)=1.000/1.000
[v2_c fold 1] M3 seed=3407 best_epoch=45 val=8.4024 C_row(audio/vision)=0.652/0.951
[v2_c fold 1] M4 seed=3407 best_epoch=42 val=8.2771 C_row(audio/vision)=0.893/0.994
[v2_c fold 1] M3 seed=2026 best_epoch=30 val=8.5775 C_row(audio/vision)=0.795/0.983
[v2_c fold 1] M4 seed=2026 best_epoch=3 val=9.4722 C_row(audio/vision)=1.000/1.000
[v2_c fold 2/5] training M3/M4 with 80 train and 20 validation clips
[v2_c fold 2] M3 seed=42 best_epoch=1 val=9.4566 C_row(audio/vision)=0.995/0.999
[v2_c fold 2] M4 seed=42 best_epoch=1 val=9.3218 C_row(audio/vision)=1.000/1.000
[v2_c fold 2] M3 seed=3407 best_epoch=26 val=9.0674 C_row(audio/vision)=0.800/0.991
[v2_c fold 2] M4 seed=3407 best_epoch=28 val=8.6381 C_row(audio/vision)=0.888/1.000
[v2_c fold 2] M3 seed=2026 best_epoch=30 val=8.9090 C_row(audio/vision)=0.736/0.985
[v2_c fold 2] M4 seed=2026 best_epoch=1 val=9.3429 C_row(audio/vision)=1.000/1.000
[v2_c fold 3/5] training M3/M4 with 80 train and 20 validation clips
[v2_c fold 3] M3 seed=42 best_epoch=26 val=8.9050 C_row(audio/vision)=0.842/0.981
[v2_c fold 3] M4 seed=42 best_epoch=31 val=8.7508 C_row(audio/vision)=0.932/0.998
[v2_c fold 3] M3 seed=3407 best_epoch=24 val=8.9361 C_row(audio/vision)=0.862/0.984
[v2_c fold 3] M4 seed=3407 best_epoch=28 val=8.3848 C_row(audio/vision)=0.926/0.998
[v2_c fold 3] M3 seed=2026 best_epoch=26 val=8.9320 C_row(audio/vision)=0.809/0.978
[v2_c fold 3] M4 seed=2026 best_epoch=3 val=9.4028 C_row(audio/vision)=1.000/1.000
[v2_c fold 4/5] training M3/M4 with 80 train and 20 validation clips
[v2_c fold 4] M3 seed=42 best_epoch=25 val=8.9781 C_row(audio/vision)=0.811/0.985
[v2_c fold 4] M4 seed=42 best_epoch=1 val=9.3892 C_row(audio/vision)=1.000/1.000
[v2_c fold 4] M3 seed=3407 best_epoch=30 val=8.9323 C_row(audio/vision)=0.780/0.964
[v2_c fold 4] M4 seed=3407 best_epoch=2 val=9.4197 C_row(audio/vision)=1.000/1.000
[v2_c fold 4] M3 seed=2026 best_epoch=39 val=8.9793 C_row(audio/vision)=0.639/0.943
[v2_c fold 4] M4 seed=2026 best_epoch=2 val=9.4643 C_row(audio/vision)=1.000/1.000
[v2_c fold 5/5] training M3/M4 with 80 train and 20 validation clips
[v2_c fold 5] M3 seed=42 best_epoch=43 val=8.7819 C_row(audio/vision)=0.707/0.888
[v2_c fold 5] M4 seed=42 best_epoch=42 val=8.3535 C_row(audio/vision)=0.861/0.995
[v2_c fold 5] M3 seed=3407 best_epoch=42 val=8.7810 C_row(audio/vision)=0.730/0.903
[v2_c fold 5] M4 seed=3407 best_epoch=27 val=8.4597 C_row(audio/vision)=0.974/1.000
[v2_c fold 5] M3 seed=2026 best_epoch=28 val=8.7653 C_row(audio/vision)=0.847/0.965
[v2_c fold 5] M4 seed=2026 best_epoch=1 val=9.2652 C_row(audio/vision)=1.000/1.000
[v2_c done] elapsed=360.1s output=/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_v2/v2_c; summary rows=48
[all done] elapsed=1086.4s output=/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_v2
{
"created_utc": "2026-09-23T10:04:06.673162+00:00",
"sample_count": 100,
"group_count": 37,
"folds": 5,
"seeds": [
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2026
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"device": "cuda",
"gpu_name": "NVIDIA GeForce RTX 5070 Ti",
"python": "3.14.7",
"torch": "2.14.0+cu130",
"feature_dir": "/home/gloamxun/modeling_zhaocui/Q1/outputs/q1_features/features",
"baseline_dir": "/home/gloamxun/modeling_zhaocui/Q1/outputs/method_comparison",
"variants": [
{
"variant": "v2_a",
"loss_coefficients": {
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"lambda_contrastive": 1.0,
"lambda_monotonicity": 0.1,
"lambda_span": 5.0,
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"coverage_floor": 0.7,
"diversity_slot_separation": 6,
"band_margin": 0.1
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2026
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"elapsed_seconds": 334.72593688964844,
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"limits": [
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"The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.",
"No human event timestamps are available, so temporal probes do not replace manual annotation.",
"Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference."
]
},
{
"variant": "v2_b",
"loss_coefficients": {
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2026
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"gpu_name": "NVIDIA GeForce RTX 5070 Ti",
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"retrieval_top_k": 3,
"shuffled_reconstruction_repeats": 5,
"masked_block_ratio": 0.2
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"Retrieval projections are fitted on training-fold grid-slot positives; test candidates are restricted to the same held-out clip.",
"The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.",
"No human event timestamps are available, so temporal probes do not replace manual annotation.",
"Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference."
]
},
{
"variant": "v2_c",
"loss_coefficients": {
"lambda_reconstruction": 1.0,
"lambda_contrastive": 1.0,
"lambda_monotonicity": 0.1,
"lambda_span": 5.0,
"lambda_diversity": 0.5,
"lambda_band": 10.0,
"coverage_floor": 0.7,
"diversity_slot_separation": 6,
"band_margin": 0.1
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"video_group_folds": 5,
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3407,
2026
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"device": "cuda",
"gpu_name": "NVIDIA GeForce RTX 5070 Ti",
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"elapsed_seconds": 360.13046407699585,
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},
"limits": [
"Retrieval projections are fitted on training-fold grid-slot positives; test candidates are restricted to the same held-out clip.",
"The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.",
"No human event timestamps are available, so temporal probes do not replace manual annotation.",
"Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference."
]
}
],
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"fixed_methods_unchanged": [
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}
deep_learning/Q1/outputs/alignment_v2/fixed_baselines/README.md
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# Fixed M1/M2 reference probes
M1 and M2 are recomputed from the same saved video-grouped folds and unchanged. The within-clip retrieval projection is fitted on each training fold; held-out candidates come only from the same clip. The reconstruction decoder is trained on aligned training representations, then compared with a control that shuffles the two non-target streams.
deep_learning/Q1/outputs/alignment_v2/fixed_baselines/alignment_metrics.csv
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method,seed,fold,sample_id,modality,mvr,normalized_entropy,width80_source_positions,c_row,c_far,expected_time_start_s,expected_time_end_s,trajectory_span_fraction
M1,fixed,1,-3g5yACwYnA/13,text,0.0,0.0,1.0,0.051428571343421936,0.0,0.282499993577376,4.7425000263558825,0.8109090328216553
M1,fixed,1,-3g5yACwYnA/13,audio,0.0,0.33763712644577026,8.279999732971191,0.0,0.0,0.13500000237036497,4.839999611456156,0.8554544448852539
M1,fixed,1,-3g5yACwYnA/13,vision,0.0,0.0041602919809520245,1.0199999809265137,0.03404662013053894,0.0,0.20000000578821822,4.799999975004455,0.8363635540008545
M1,fixed,1,-3g5yACwYnA/3,text,0.0,0.0,1.0,0.017142856493592262,0.0,0.08249999371173528,14.09249920135643,0.9751740097999573
M1,fixed,1,-3g5yACwYnA/3,audio,0.0,0.44154608249664307,23.219999313354492,0.0,0.0,0.05499999803782363,14.150000235951026,0.9810905456542969
M1,fixed,1,-3g5yACwYnA/3,vision,0.0,0.07565683126449585,1.5199999809265137,0.003265306120738387,0.0,0.0,14.199999050645351,0.9883990287780762
M1,fixed,1,-3g5yACwYnA/2,text,0.0,0.0,1.0,0.055510204285383224,0.0,0.20249999643196104,8.232499743585983,0.8572953939437866
M1,fixed,1,-3g5yACwYnA/2,audio,0.0,0.3921087086200714,14.9399995803833,0.0,0.0,0.22499998634285914,8.909999633644977,0.9272242188453674
M1,fixed,1,-3g5yACwYnA/2,vision,0.0,0.029416846111416817,1.1799999475479126,0.014693877659738064,0.0,0.19999999561444173,8.899999421013593,0.9288256168365479
M1,fixed,1,-3g5yACwYnA/9,text,0.0,0.0,1.0,0.03346938639879227,0.0,0.03249999892138053,8.382500060026864,0.9488636255264282
M1,fixed,1,-3g5yACwYnA/9,audio,0.0,0.39564627408981323,14.220000267028809,0.0,0.0,0.025000001692027783,8.534999789237817,0.9670453667640686
M1,fixed,1,-3g5yACwYnA/9,vision,0.0,0.03297046199440956,1.1799999475479126,0.012583031319081783,0.0,0.0,8.600000516492447,0.9772727489471436
M1,fixed,1,-egA8-b7-3M/26,text,0.0,0.0,1.0,0.051428571343421936,0.0,0.29249998316942033,5.062499923622111,0.7949999570846558
M1,fixed,1,-egA8-b7-3M/26,audio,0.0,0.3478807806968689,8.899999618530273,0.0,0.0,0.09000000298834313,5.180000354534983,0.8483333587646484
M1,fixed,1,-egA8-b7-3M/26,vision,0.0,0.0,1.0,0.02857142873108387,0.0,0.0,5.19999962131683,0.8666665554046631
M1,fixed,1,-egA8-b7-3M/17,text,0.0,0.0,1.0,0.04571428522467613,0.0,0.6525000228189155,6.362500157163838,0.7857798933982849
M1,fixed,1,-egA8-b7-3M/17,audio,0.0,0.34084564447402954,9.920000076293945,0.0,0.0,0.6399999757085963,6.479999733746707,0.8036697506904602
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M1,fixed,5,-MeTTeMJBNc/0,audio,0.0,0.3866513669490814,12.640000343322754,0.0,0.0,1.0150001446406047,8.50499939918518,0.8024998903274536
M1,fixed,5,-MeTTeMJBNc/0,vision,0.0,0.01080187875777483,1.059999942779541,0.012583031319081783,0.0,1.0000000099341075,8.600000460942587,0.8142857551574707
M1,fixed,5,-MeTTeMJBNc/13,text,0.0,0.0,1.0,0.055510204285383224,0.0,0.3925000075390935,4.692500284892121,0.7962962985038757
M1,fixed,5,-MeTTeMJBNc/13,audio,0.0,0.3221759796142578,7.760000228881836,0.0,0.0,0.36000004099768645,4.815000211391528,0.824999988079071
M1,fixed,5,-MeTTeMJBNc/13,vision,0.0,0.004206198267638683,1.0199999809265137,0.04139355570077896,0.0,0.40000002767159043,4.800000332059085,0.8148148059844971
M1,fixed,5,-MeTTeMJBNc/7,text,0.0,0.0,1.0,0.015510204248130322,0.0,0.2124999940800443,9.62249962240694,0.8961904048919678
M1,fixed,5,-MeTTeMJBNc/7,audio,0.0,0.40608009696006775,16.1200008392334,0.0,0.0,0.1850000026692661,9.700000355620224,0.9061904549598694
M1,fixed,5,-MeTTeMJBNc/7,vision,0.0,0.03491668775677681,1.2000000476837158,0.009317724965512753,0.0,0.2000000139860517,9.700000981469014,0.9047619700431824
M1,fixed,5,-yRb-Jum7EQ/1,text,0.0,0.014248286373913288,1.0800000429153442,0.0028861500322818756,0.0,0.5224999532197937,28.5624995554775,0.9580865502357483
M1,fixed,5,-yRb-Jum7EQ/1,audio,0.0,0.4908865690231323,45.7400016784668,0.0,0.0,0.5299999632423266,28.55500025412935,0.9575740694999695
M1,fixed,5,-yRb-Jum7EQ/1,vision,0.0,0.18275710940361023,2.7200000286102295,0.0005772300064563751,0.0,0.6000000386148281,28.5000001442885,0.9533029794692993
M1,fixed,5,-yRb-Jum7EQ/5,text,0.0,0.0,1.0,0.020408162847161293,0.0,0.4925000257282878,12.762500320628881,0.9318987727165222
M1,fixed,5,-yRb-Jum7EQ/5,audio,0.0,0.4198280870914459,20.940000534057617,0.0,0.0,0.3149999949186252,12.860000831157823,0.9527848958969116
M1,fixed,5,-yRb-Jum7EQ/5,vision,0.0,0.06867483258247375,1.4800000190734863,0.007818245328962803,0.0,0.3000000220211204,12.800000154773334,0.949367105960846
M1,fixed,5,-yRb-Jum7EQ/6,text,0.0,0.0,1.0,0.0367346927523613,0.0,0.6724999747153496,10.742499861953577,0.8991070985794067
M1,fixed,5,-yRb-Jum7EQ/6,audio,0.0,0.40597784519195557,17.360000610351562,0.0,0.0,0.5199999427585217,10.8849997899007,0.9254463911056519
M1,fixed,5,-yRb-Jum7EQ/6,vision,0.0,0.0439266674220562,1.2799999713897705,0.010612244717776775,0.0,0.5999999820943549,10.7999998445914,0.9107142686843872
M2,fixed,5,-3nNcZdcdvU/5,text,0.0,0.0,1.0,0.0636734664440155,0.006060606334358454,1.0425000404648632,7.06250007426395,0.7652542591094971
M2,fixed,5,-3nNcZdcdvU/5,audio,0.0,0.4099470376968384,12.539999961853027,0.0,0.0,0.08000000115502345,7.7298203610307965,0.9724348187446594
M2,fixed,5,-3nNcZdcdvU/5,vision,0.0,0.0,1.0,0.008163264952600002,0.0,0.0,7.80000004301395,0.9915254712104797
M2,fixed,5,-NFrJFQijFE/1,text,0.0,0.004999999888241291,1.0199999809265137,0.07193577289581299,0.004040404222905636,0.36250003457069396,5.502500286102294,0.8986014127731323
M2,fixed,5,-NFrJFQijFE/1,audio,0.0,0.37702804803848267,9.260000228881836,0.0,0.0,0.059999999329447745,5.6294375109672545,0.9736778736114502
M2,fixed,5,-NFrJFQijFE/1,vision,0.0,0.0,1.0,0.017142856493592262,0.0,0.0,5.600000193119048,0.9790210127830505
M2,fixed,5,-NFrJFQijFE/2,text,0.0,0.0,1.0,0.0636734664440155,0.006060606334358454,1.1824998718500137,6.612500052452087,0.7938596606254578
M2,fixed,5,-NFrJFQijFE/2,audio,0.0,0.3928936719894409,11.380000114440918,0.00027210882399231195,0.0,0.07000000689178705,6.669155902862549,0.9647888541221619
M2,fixed,5,-NFrJFQijFE/2,vision,0.0,0.0,1.0,0.013061224482953548,0.0,0.0,6.599999692440033,0.9649122357368469
M2,fixed,5,-a55Q6RWvTA/3,text,0.0,0.07224695384502411,1.4600000381469727,0.006052419077605009,0.0,0.42249999095996227,21.402500788370773,0.9478915929794312
M2,fixed,5,-a55Q6RWvTA/3,audio,0.0,0.4917435348033905,35.84000015258789,0.0,0.0,0.2249999967714151,21.870475514729822,0.9779582023620605
M2,fixed,5,-a55Q6RWvTA/3,vision,0.0,0.1661204844713211,2.2200000286102295,0.0,0.0,0.20000001316269242,21.900000317891443,0.9804217219352722
M2,fixed,5,-dxfTGcXJoc/1,text,0.0,0.019342640414834023,1.100000023841858,0.015708278864622116,0.0,0.6525000443061194,15.242500543594364,0.9043388962745667
M2,fixed,5,-dxfTGcXJoc/1,audio,0.0,0.46907928586006165,26.0,0.0,0.0,0.1649999928971132,15.896191676457725,0.9750738739967346
M2,fixed,5,-dxfTGcXJoc/1,vision,0.0,0.09779410809278488,1.6200000047683716,0.0,0.0,0.09999999956538282,16.033333341280624,0.9876033067703247
M2,fixed,5,-dxfTGcXJoc/0,text,0.0,0.022398287430405617,1.1200000047683716,0.013399357907474041,0.0,0.43250002463658654,19.542500464121503,0.9306818246841431
M2,fixed,5,-dxfTGcXJoc/0,audio,0.0,0.48628562688827515,32.81999969482422,0.0,0.0,0.21000001008311908,20.250609238942467,0.9760037064552307
M2,fixed,5,-dxfTGcXJoc/0,vision,0.0,0.15480411052703857,2.059999942779541,0.0,0.0,0.20000001415610316,20.30000022252401,0.9788961410522461
M2,fixed,5,-dxfTGcXJoc/2,text,0.0,0.00786246545612812,1.0399999618530273,0.018775509670376778,0.0,0.6024999423524477,15.642500604266784,0.9024000763893127
M2,fixed,5,-dxfTGcXJoc/2,audio,0.0,0.47225603461265564,27.200000762939453,0.0,0.0,0.17000001825635386,16.465641725562854,0.9777384996414185
M2,fixed,5,-dxfTGcXJoc/2,vision,0.0,0.10637769103050232,1.6799999475479126,0.0,0.0,0.1000000066302557,16.499999828945718,0.984000027179718
M2,fixed,5,-dxfTGcXJoc/6,text,0.0,0.0,1.0,0.027755102142691612,0.0,0.6725000135600566,11.852499624093372,0.8780104517936707
M2,fixed,5,-dxfTGcXJoc/6,audio,0.0,0.45111221075057983,20.760000228881836,0.0,0.0,0.1299999924376607,12.531746701399484,0.9739591479301453
M2,fixed,5,-dxfTGcXJoc/6,vision,0.0,0.046666670590639114,1.2799999713897705,0.0,0.0,0.09999999509503443,12.600000496705372,0.9816754460334778
M2,fixed,5,-mJ2ud6oKI8/1,text,0.0,0.0,1.0,0.12326530367136002,0.031313132494688034,0.35249996886761986,6.012499743664404,0.9320349097251892
M2,fixed,5,-mJ2ud6oKI8/1,audio,0.0,0.38793665170669556,10.039999961853027,0.0,0.0,0.06499999337683184,5.987874667798977,0.9753226637840271
M2,fixed,5,-mJ2ud6oKI8/1,vision,0.0,0.0,1.0,0.015510204248130322,0.0,0.0,6.005999971684814,0.9890109300613403
M2,fixed,5,-mJ2ud6oKI8/2,text,0.0,0.0,1.0,0.13469387590885162,0.042424242943525314,0.03250000044829321,5.262500008618474,0.9166272878646851
M2,fixed,5,-mJ2ud6oKI8/2,audio,0.0,0.38236555457115173,9.34000015258789,0.0,0.0,0.06000000041886078,5.632263832847615,0.9766135811805725
M2,fixed,5,-mJ2ud6oKI8/2,vision,0.0,0.0,1.0,0.017142856493592262,0.0,0.0,5.6056000543657944,0.9824562072753906
M2,fixed,5,-mJ2ud6oKI8/6,text,0.0,0.0,1.0,0.20163264870643616,0.0727272778749466,0.20249999715089798,1.9924999917070072,0.8006918430328369
M2,fixed,5,-mJ2ud6oKI8/6,audio,0.0,0.26947706937789917,3.9200000762939453,0.0,0.0,0.02500000095916912,2.199062652895848,0.9724884033203125
M2,fixed,5,-mJ2ud6oKI8/6,vision,0.0,0.0,1.0,0.06857142597436905,0.0,0.0,2.2022000278433165,0.9850746393203735
M2,fixed,5,-mJ2ud6oKI8/9,text,0.0,0.004484876524657011,1.0199999809265137,0.03625650331377983,0.0,0.1625000037714684,6.772500204369585,0.9433424472808838
M2,fixed,5,-mJ2ud6oKI8/9,audio,0.0,0.3957805633544922,11.760000228881836,0.0,0.0,0.07500000425059193,6.879375098672748,0.9710825681686401
M2,fixed,5,-mJ2ud6oKI8/9,vision,0.0,0.0,1.0,0.01224489789456129,0.0,0.0,6.8068001057730525,0.971428632736206
M2,fixed,5,-mJ2ud6oKI8/8,text,0.0,0.0,1.0,0.11591836810112,0.0363636389374733,0.9725000593069245,3.9425003060945474,0.7120879292488098
M2,fixed,5,-mJ2ud6oKI8/8,audio,0.0,0.34992116689682007,7.260000228881836,0.0,0.0,0.04500000409233291,4.113137338462114,0.9753775000572205
M2,fixed,5,-mJ2ud6oKI8/8,vision,0.0,0.0,1.0,0.031020408496260643,0.0,0.0,4.004000230503618,0.9599999785423279
M2,fixed,5,-tANM6ETl_M/3,text,0.0,0.004484876524657011,1.0199999809265137,0.038705483078956604,0.0,0.6125000432133675,6.092499784628551,0.8138613700866699
M2,fixed,5,-tANM6ETl_M/3,audio,0.0,0.39175882935523987,11.180000305175781,0.0002581450971774757,0.0,0.07000000923871993,6.576813141504924,0.9663583636283875
M2,fixed,5,-tANM6ETl_M/3,vision,0.0,0.0,1.0,0.013061224482953548,0.0,0.0,6.6333335638046265,0.985148549079895
M2,fixed,5,-MeTTeMJBNc/0,text,0.0,0.00455340463668108,1.0199999809265137,0.046052418649196625,0.001010101055726409,1.0925000409285228,8.412500619888306,0.7842857837677002
M2,fixed,5,-MeTTeMJBNc/0,audio,0.0,0.42355871200561523,15.399999618530273,0.0,0.0,0.09500000129143397,9.166385809580486,0.9719341993331909
M2,fixed,5,-MeTTeMJBNc/0,vision,0.0,0.0,1.0,0.0024489795323461294,0.0,0.0,9.200000286102295,0.985714316368103
M2,fixed,5,-MeTTeMJBNc/13,text,0.0,0.0,1.0,0.07265306264162064,0.003030303167179227,0.3925000075390935,4.692500284892121,0.7962962985038757
M2,fixed,5,-MeTTeMJBNc/13,audio,0.0,0.3684433698654175,8.520000457763672,0.00047130632447078824,0.0,0.05500000682232901,5.227750039936165,0.9579166173934937
M2,fixed,5,-MeTTeMJBNc/13,vision,0.0,0.0,1.0,0.019591836258769035,0.0,0.0,5.200000118331849,0.9629629254341125
M2,fixed,5,-MeTTeMJBNc/7,text,0.0,0.004036981612443924,1.0199999809265137,0.022857142612338066,0.0,0.2124999940800443,9.62249962240694,0.8961904048919678
M2,fixed,5,-MeTTeMJBNc/7,audio,0.0,0.4358333349227905,16.799999237060547,0.0,0.0,0.10500001076528771,10.331739634441613,0.9739751815795898
M2,fixed,5,-MeTTeMJBNc/7,vision,0.0,0.010475005954504013,1.059999942779541,0.0,0.0,0.10000000699302584,10.399999710270404,0.9809523224830627
M2,fixed,5,-yRb-Jum7EQ/1,text,0.0,0.03414232283830643,1.2000000476837158,0.007917283102869987,0.0,0.5224999532197937,28.5624995554775,0.9580865502357483
M2,fixed,5,-yRb-Jum7EQ/1,audio,0.0,0.5092304348945618,47.20000076293945,0.0,0.0,0.2950000217112945,28.875521684626918,0.9765554070472717
M2,fixed,5,-yRb-Jum7EQ/1,vision,0.0,0.21393641829490662,2.9200000762939453,0.0,0.0,0.20000001287160937,28.89999951587075,0.980637788772583
M2,fixed,5,-yRb-Jum7EQ/5,text,0.0,0.0043067652732133865,1.0199999809265137,0.030204081907868385,0.0,0.4925000257282878,12.762500320628881,0.9318987727165222
M2,fixed,5,-yRb-Jum7EQ/5,audio,0.0,0.4534182548522949,21.059999465942383,0.0,0.0,0.13500001358822827,12.956728319134136,0.9738021492958069
M2,fixed,5,-yRb-Jum7EQ/5,vision,0.0,0.052941616624593735,1.3200000524520874,0.0,0.0,0.10000000120916668,13.000000316603044,0.9797468781471252
M2,fixed,5,-yRb-Jum7EQ/6,text,0.0,0.0,1.0,0.04979591816663742,0.0,0.6724999747153496,10.742499861953577,0.8991070985794067
M2,fixed,5,-yRb-Jum7EQ/6,audio,0.0,0.44265732169151306,18.3799991607666,0.0,0.0,0.1149999965680847,11.085000368057688,0.9794642925262451
M2,fixed,5,-yRb-Jum7EQ/6,vision,0.0,0.02066345140337944,1.1200000047683716,0.0,0.0,0.09999999701572582,10.999999755176345,0.9732142090797424
1 method seed fold sample_id modality mvr normalized_entropy width80_source_positions c_row c_far expected_time_start_s expected_time_end_s trajectory_span_fraction
2 M1 fixed 1 -3g5yACwYnA/13 text 0.0 0.0 1.0 0.051428571343421936 0.0 0.282499993577376 4.7425000263558825 0.8109090328216553
3 M1 fixed 1 -3g5yACwYnA/13 audio 0.0 0.33763712644577026 8.279999732971191 0.0 0.0 0.13500000237036497 4.839999611456156 0.8554544448852539
4 M1 fixed 1 -3g5yACwYnA/13 vision 0.0 0.0041602919809520245 1.0199999809265137 0.03404662013053894 0.0 0.20000000578821822 4.799999975004455 0.8363635540008545
5 M1 fixed 1 -3g5yACwYnA/3 text 0.0 0.0 1.0 0.017142856493592262 0.0 0.08249999371173528 14.09249920135643 0.9751740097999573
6 M1 fixed 1 -3g5yACwYnA/3 audio 0.0 0.44154608249664307 23.219999313354492 0.0 0.0 0.05499999803782363 14.150000235951026 0.9810905456542969
7 M1 fixed 1 -3g5yACwYnA/3 vision 0.0 0.07565683126449585 1.5199999809265137 0.003265306120738387 0.0 0.0 14.199999050645351 0.9883990287780762
8 M1 fixed 1 -3g5yACwYnA/2 text 0.0 0.0 1.0 0.055510204285383224 0.0 0.20249999643196104 8.232499743585983 0.8572953939437866
9 M1 fixed 1 -3g5yACwYnA/2 audio 0.0 0.3921087086200714 14.9399995803833 0.0 0.0 0.22499998634285914 8.909999633644977 0.9272242188453674
10 M1 fixed 1 -3g5yACwYnA/2 vision 0.0 0.029416846111416817 1.1799999475479126 0.014693877659738064 0.0 0.19999999561444173 8.899999421013593 0.9288256168365479
11 M1 fixed 1 -3g5yACwYnA/9 text 0.0 0.0 1.0 0.03346938639879227 0.0 0.03249999892138053 8.382500060026864 0.9488636255264282
12 M1 fixed 1 -3g5yACwYnA/9 audio 0.0 0.39564627408981323 14.220000267028809 0.0 0.0 0.025000001692027783 8.534999789237817 0.9670453667640686
13 M1 fixed 1 -3g5yACwYnA/9 vision 0.0 0.03297046199440956 1.1799999475479126 0.012583031319081783 0.0 0.0 8.600000516492447 0.9772727489471436
14 M1 fixed 1 -egA8-b7-3M/26 text 0.0 0.0 1.0 0.051428571343421936 0.0 0.29249998316942033 5.062499923622111 0.7949999570846558
15 M1 fixed 1 -egA8-b7-3M/26 audio 0.0 0.3478807806968689 8.899999618530273 0.0 0.0 0.09000000298834313 5.180000354534983 0.8483333587646484
16 M1 fixed 1 -egA8-b7-3M/26 vision 0.0 0.0 1.0 0.02857142873108387 0.0 0.0 5.19999962131683 0.8666665554046631
17 M1 fixed 1 -egA8-b7-3M/17 text 0.0 0.0 1.0 0.04571428522467613 0.0 0.6525000228189155 6.362500157163838 0.7857798933982849
18 M1 fixed 1 -egA8-b7-3M/17 audio 0.0 0.34084564447402954 9.920000076293945 0.0 0.0 0.6399999757085963 6.479999733746707 0.8036697506904602
19 M1 fixed 1 -egA8-b7-3M/17 vision 0.0 0.013868527486920357 1.0800000429153442 0.028332332149147987 0.0 0.6000000415191079 6.399999865367731 0.7981651425361633
20 M1 fixed 1 -egA8-b7-3M/18 text 0.0 0.0 1.0 0.02857142873108387 0.0 0.6324999500151997 7.752499492055952 0.8509960174560547
21 M1 fixed 1 -egA8-b7-3M/18 audio 0.0 0.38084501028060913 12.140000343322754 0.0 0.0 0.609999956474714 7.819999971369584 0.8617530465126038
22 M1 fixed 1 -egA8-b7-3M/18 vision 0.0 0.011126941069960594 1.059999942779541 0.015271108597517014 0.0 0.5999999939770102 7.799999921701133 0.8605577945709229
23 M1 fixed 1 -egA8-b7-3M/16 text 0.0 0.0 1.0 0.08571428805589676 0.002020202111452818 0.6225000247359276 4.592500348885854 0.7174699306488037
24 M1 fixed 1 -egA8-b7-3M/16 audio 0.0 0.3220002353191376 7.300000190734863 0.0 0.0 0.5200000301003456 4.770000418027242 0.7680723667144775
25 M1 fixed 1 -egA8-b7-3M/16 vision 0.0 0.0 1.0 0.03591836616396904 0.0 0.6000000188748041 4.800000150998433 0.7590361833572388
26 M1 fixed 1 -egA8-b7-3M/13 text 0.0 0.0 1.0 0.07510203868150711 0.0 0.4324999912783479 3.3824999904683235 0.7080000638961792
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