From d014c9438b432cbd9658f15798db284d75c539ae Mon Sep 17 00:00:00 2001 From: Franklin Koch Date: Thu, 3 Oct 2024 17:31:09 -0600 Subject: [PATCH 1/2] =?UTF-8?q?=F0=9F=94=A7=20Add=20doi,=20venue,=20fundin?= =?UTF-8?q?g=20to=20metadata=20and=20tidy=20author=20names?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- index.md | 2 +- myst.yml | 41 +++++++++++++++++++++++++++-------------- 2 files changed, 28 insertions(+), 15 deletions(-) diff --git a/index.md b/index.md index dc5600a..56d4f96 100644 --- a/index.md +++ b/index.md @@ -13,7 +13,7 @@ This paper reports the application of deep learning techniques in bright-field t +++{"part":"epigraph"} :::{admonition} Co-First Authors -The authors marked with `#` contributed equally to this work as co-first authors. +Yifei Duan and Yifan Duan contributed equally to this work as co-first authors. ::: +++ diff --git a/myst.yml b/myst.yml index 0a174c6..9f2adab 100644 --- a/myst.yml +++ b/myst.yml @@ -1,6 +1,7 @@ # See docs at: https://mystmd.org/guide/frontmatter version: 1 project: + doi: 10.69761/HDTA8338 id: msa-em-microscopy-seg-track-1 title: 'Deep Learning Applications in Microscopy: Segmentation and Tracking' description: Comparative analysis of recent computer vision models in microscopic image segmentation (including EfficientSAM-tiny, YOLOv8n-seg, Swin-UNet, and VMamba). Integration of EfficientSAM-tiny and DeAOT for video tracking in high-temperature sintering processes under a microscope. @@ -29,6 +30,10 @@ project: history: submitted: 2024-09-19 published: 2024-10-03 + venue: + title: Elemental Microscopy + short_title: EM + doi: 10.69761/EM requirements: - 'environment.yml' resources: @@ -41,7 +46,7 @@ project: - README.md authors: - id: yifeiduan - name: 'Yifei Duan #' + name: Yifei Duan equal_contributor: true email: yifeid@alumni.upenn.edu # https://orcid.org/ @@ -53,9 +58,9 @@ project: address: 3231 Walnut Street, Philadelphia, Pennsylvania, USA, 19104 # https://ror.org/00b30xv10 ror: 00b30xv10 - roles: + roles: # https://credit.niso.org/ - - Conceptualization + - Conceptualization - Data curation - Formal analysis - Investigation @@ -66,7 +71,7 @@ project: - Visualization - Writing - original draft - id: yifanduan - name: 'Yifan Duan #' + name: Yifan Duan equal_contributor: true email: ivanduan@berkeley.edu orcid: 0009-0008-2922-3028 @@ -120,16 +125,24 @@ project: - Resources - Supervision - Writing - review & editing - # funding: - # - statement: - # recipients: - # - - # - statement: - # id: - # sources: - # - name: - # recipients: - # - + funding: + - statement: | + This work was carried out in part at the Singh Center for Nanotechnology, + which is supported by the NSF National Nanotechnology Coordinated Infrastructure + Program under grant NNCI-2025608 and through the use of facilities supported by + the University of Pennsylvania Materials Research Science and Engineering + Center (MRSEC) DMR-2309043. + awards: + - id: NNCI-2025608 + source: NSF National Nanotechnology Coordinated Infrastructure Program + - id: DMR-2309043 + source: University of Pennsylvania Materials Research Science and Engineering Center + - statement: | + C.-Y. C. and E.A.S. acknowledge additional support through the NSF Division of + Materials Research's Metals and Metallic Nanostructures program, DMR-2303084. + awards: + - id: DMR-2303084 + source: NSF Division of Materials Research's Metals and Metallic Nanostructures abbreviations: CLIP: Contrastive Language-Image Pre-training CNN: Convolutional Neural Networks From 84136732a5006abf408935030fdba4fe42e8a773 Mon Sep 17 00:00:00 2001 From: Franklin Koch Date: Thu, 3 Oct 2024 23:14:10 -0600 Subject: [PATCH 2/2] =?UTF-8?q?=F0=9F=94=A7=20Move=20contributor=20note=20?= =?UTF-8?q?from=20content=20to=20frontmatter?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- index.md | 7 ------- myst.yml | 2 ++ 2 files changed, 2 insertions(+), 7 deletions(-) diff --git a/index.md b/index.md index 56d4f96..9407602 100644 --- a/index.md +++ b/index.md @@ -11,13 +11,6 @@ This paper reports the application of deep learning techniques in bright-field t +++ -+++{"part":"epigraph"} -:::{admonition} Co-First Authors -Yifei Duan and Yifan Duan contributed equally to this work as co-first authors. -::: -+++ - - +++{"part":"epigraph"} :::{warning} Pre-print This article has not yet been peer-reviewed. diff --git a/myst.yml b/myst.yml index 9f2adab..80eefa5 100644 --- a/myst.yml +++ b/myst.yml @@ -70,6 +70,7 @@ project: - Validation - Visualization - Writing - original draft + note: Yifei Duan and Yifan Duan contributed equally to this work as co-first authors. - id: yifanduan name: Yifan Duan equal_contributor: true @@ -91,6 +92,7 @@ project: - Visualization - Writing - original draft - Writing - review & editing + note: Yifei Duan and Yifan Duan contributed equally to this work as co-first authors. - id: zequnhe name: Zequn He email: hezequn@seas.upenn.edu