Leaderboard link - #29
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TonyBagnall wants to merge 2 commits into
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TonyBagnall wants to merge 2 commits into
TonyBagnall wants to merge 2 commits into
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DisjointCNN, TimesURL and XCM were the last three without it, and all seven of the Hali run's estimators returned. EmoPain joins the scored set, taking it from 56 to 57 of 64, and DisjointCNN, TimesURL and XCM each reach 66. The dataset never needed rescaling. aeon's check_collection_variance flags pairs with std <= 1e-7 and a nonzero range, and all 1733 of EmoPain's sit in channels 13 to 25, which are sparse rather than mis-scaled: 84% of their values are below 1e-5 while their peaks reach 1.55, the same range as the motion channels. aeon relegated the check to a warning in #3598, and running on Hali with an aeon past that commit was the whole fix. XCM's result carries 'window_size': 0.8, so it is the fixed-parameter run the other 65 use rather than the cross-validated search the lookup name now resolves to. That was pinned in the Hali configuration for exactly this reason. Accuracies span 0.628 to 0.848, with PatchMTSC and TimesURL highest and DisjointCNN lowest, so nothing looks degenerate despite the flat channels. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The ledger said "OOM at 64GB, exhausted the top memory tier (64GB)". Both halves are wrong. The attempts reached 128 GB, and at that tier the job does not run out of memory: it dies with "Segmentation fault (core dumped)" and no Python traceback, three times. The confusion is understandable. The five attempts at and below 64 GB were genuine OOM kills, and the controller classifies a job that vanishes without writing an error as OOM, which is the right default for a kill and the wrong one for a segfault. It matters because it changes what closes USCActivity. More memory has already been tried and does not help; this needs a reproduction and a fix in RDST or below it. Two details narrow it: USCActivity is 43060 cases, and RDST segfaults on exactly one other dataset in the archive, PPGDalia_disc, which is also large-case, so a numba routine indexing past the end once the case count is large is the obvious suspect rather than anything about memory. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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results/schema.md.