[logseq-plugin-git:commit] 2026-01-18T14:49:06.071Z

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icon:: ✏️
generation_time:: [[2026-01-17]] T15:00:45Z
generation_time:: [[2026-01-18]] T14:00:45Z
- # **TODOs and IDEAs**
- {{query (and [[TODO]] [[KaraKeep-Highlights]] (or [[Ideas]] [[KaraKeep-Highlights]]))}}
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todoist-id:: [6fmMjW88FQ9m8x2g](https://todoist.com/showTask?id=6fmMjW88FQ9m8x2g)
todoist-project:: #✏️ PAPERS
todoist-due:: 2026-01-17
todoist-desc:: WebLink: [Outlook](https://outlook.office365.com/owa/?ItemID=AAMkADM1NGNiNjk0LTY0ZGUtNDgzOC04MDM5LWNhODNkYWNjNjU4YwBGAAAAAACT6qp78kRgRKuUMBdWEga%2FBwCOhWlC8F7PRKzlljZYZYQmAAAAAAEMAACOhWlC8F7PRKzlljZYZYQmAAiaRHodAAA%3D&exvsurl=1&viewmodel=ReadMessageItem) Email Content Summary: - Initial impression and feedback request on the draft. - Recognize lack of extensive data on case study part, no certifiable evidence available. - Suggestion to clarify the exploratory nature of data and possibly cut detailed parts accordingly. - Latex file hosted on Overleaf to be shared later. - Reference to using IEEE software standards; draft might need trimming or condensing. - Discussion about restructuring submission plans due to draft length. - Deep review and improved figures needed, specifically on asymmetry and traceability matrix analysis. - Deadline implied: review needed by weekend (from email dated 14 Jan 2026). Action Points Extracted: 1. Share Overleaf latex file. 2. Review draft for trimming or condensing. 3. Clarify exploratory nature of case study data. 4. Discuss and possibly revise submission plans based on draft length. 5. Revise and improve figures. 6. Analyze traceability matrix deeply. 7. Complete review by the weekend of 17 January 2026.
todoist-desc:: WebLink: [Outlook](https://outlook.office365.com/owa/?ItemID=AAMkADM1NGNiNjk0LTY0ZGUtNDgzOC04MDM5LWNhODNkYWNjNjU4YwBGAAAAAACT6qp78kRgRKuUMBdWEga%2FBwCOhWlC8F7PRKzlljZYZYQmAAAAAAEMAACOhWlC8F7PRKzlljZYZYQmAAiaRHodAAA%3D&exvsurl=1&viewmodel=ReadMessageItem)
todoist-labels:: #goal #logseqed
todoist-status:: ◼️
- [[2026-01-17]] Item shared with you: FSE2026-MachineUnlearning.drawio