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Summary
delete-collection (#528) does not evict rows from connected subscribers, despite the documented guarantee. The HTTP/DB
layer is correct (cascade delete, recreate-at-seq-1, owner-only gating all ...
bug
Summary
AAC audio tracks (mp4a.40.2, the de-facto standard audio codec in .mp4) fail to decode: the WebCodecs AudioDecoder is
configured successfully but emits 0 output frames and then errors. Root cause: ...
Context
Comparing the production Media Library tab to docs/dashboard-mockup.html (see #165).
Problem
- The stats bar only shows Series/Movies/Wanted counts. The mockup also has Episodes, Downloading, ...
dashboard
enhancement
media
ui
Issue: Review Text Misalignment - Review Text Not Properly Centered Description: There is a misalignment issue on
website where the text within user reviews is not properly centered within their designated ...
Description: When users perform a location search on the Enatega Multivendor Web platform, the map display is
incomplete, showing only half of the map instead of the entire area.
Steps to Reproduce: Go ...
Description
engagement_tasks and retention_tasks are present in app/tasks/ and their tasks are registered in the beat schedule in
app/celery_app.py, but neither module is imported in celery_worker.py. ...
bug
backend/app/strategies/manual/analyst_revision_momentum.py or ml_enhanced/analyst_revision_momentum.py has no
corresponding backend/tests/unit/test_analyst_revision_momentum.py.
Acceptance criteria:
...
good-first-issue
qa:missing-test
Surfaced by code review of #481 (the run-history task-outcome surface, #476). Both holes are pre-existing in core —
readTaskOutcome is the first UI surface that renders them as a false claim. The invariant ...
✨ Feature Request
Neon glow effects are popular in dark-themed UIs, gaming sites, and tech dashboards. EaseMotion has no glow or
shadow-based animation class currently.
🎯 What Should Be Added
- .neon-glow ...
animation
gssoc:approved
GSSoC-26
level:intermediate
type:feature
テーマ: 日本株市場におけるコーディング・金融数理を用いた分析
日付: 2026-06-10
- 根拠: 日本の信用取引は個人投資家比率が高く、先行研究(Sakaguchi
Hamao 2008)において信用買い残の増加が翌月リターンに対して負の予測力(逆張り特性)を持つことが報告されている。ただし同研究は線形モデルに限定されており、機械学習によって非線形効果・信用売り残との相互作用・信用倍率水準の閾値効果を捉えることで予測力を改善できるかは未検証である。 ...
auto-research
domain:finance
hypothesis

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