1 abcnet utility box
Author: H | 2025-04-24
{ payload :{ allShortcutsEnabled :false, fileTree :{ projects/ABCNet :{ items :[{ name : abcnet, path : projects/ABCNet/abcnet, contentType : directory },{ name ABCnet Translit, free and safe download. ABCnet Translit latest version: ABCnet Translit: Transliterate Kazakh Text to Latin Alphabet. ABCnet Translit
1-abc.net Utility Box
Author = {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong}, journal = {IEEE T. Pattern Analysis and Machine Intelligence (TPAMI)}, year = {2021}}@inproceedings{chen2020blendmask, title = {{BlendMask}: Top-Down Meets Bottom-Up for Instance Segmentation}, author = {Chen, Hao and Sun, Kunyang and Tian, Zhi and Shen, Chunhua and Huang, Yongming and Yan, Youliang}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2020}}@inproceedings{zhang2020MEInst, title = {Mask Encoding for Single Shot Instance Segmentation}, author = {Zhang, Rufeng and Tian, Zhi and Shen, Chunhua and You, Mingyu and Yan, Youliang}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2020}}@inproceedings{liu2020abcnet, title = {{ABCNet}: Real-time Scene Text Spotting with Adaptive {B}ezier-Curve Network}, author = {Liu, Yuliang and Chen, Hao and Shen, Chunhua and He, Tong and Jin, Lianwen and Wang, Liangwei}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2020}}@ARTICLE{9525302, author={Liu, Yuliang and Shen, Chunhua and Jin, Lianwen and He, Tong and Chen, Peng and Liu, Chongyu and Chen, Hao}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, title={ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting}, year={2021}, volume={}, number={}, pages={1-1}, doi={10.1109/TPAMI.2021.3107437}} @inproceedings{wang2020solo, title = {{SOLO}: Segmenting Objects by Locations}, author = {Wang, Xinlong and Kong, Tao and Shen, Chunhua and Jiang, Yuning and Li, Lei}, booktitle = {Proc. Eur. Conf. Computer Vision (ECCV)}, year = {2020}}@inproceedings{wang2020solov2, title = {{SOLOv2}: Dynamic and Fast Instance Segmentation}, author = {Wang, Xinlong and Zhang, Rufeng and Kong, Tao and Li, Lei and Shen, Chunhua}, booktitle = {Proc. Advances in Neural Information Processing Systems (NeurIPS)}, year = {2020}}@article{wang2021solo, title = {{SOLO}: A Simple Framework for Instance Segmentation}, author = {Wang, Xinlong and Zhang, Rufeng and Shen, Chunhua and Kong, Tao and Li, Lei}, journal = {IEEE T. Pattern Analysis and Machine Intelligence (TPAMI)}, year = {2021}}@article{tian2019directpose, title = {{DirectPose}: Direct End-to-End Multi-Person Pose Estimation}, author = {Tian, Zhi and Chen, Hao and Shen, Chunhua}, journal = {arXiv preprint arXiv:1911.07451}, year = {2019}}@inproceedings{tian2020conditional, title = {Conditional Convolutions for Instance Segmentation}, author = {Tian, Zhi and Shen, Chunhua and Chen, Hao}, booktitle = {Proc. Eur. Conf. Computer Vision (ECCV)}, year = {2020}}@inproceedings{tian2021boxinst, title = {{BoxInst}: High-Performance Instance Segmentation with Box Annotations}, author = {Tian, Zhi and Shen, Chunhua and Wang, Xinlong and Chen, Hao}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2021}}@inproceedings{wang2021densecl, title = {Dense Contrastive Learning for Self-Supervised Visual Pre-Training}, author = {Wang, Xinlong and Zhang, Rufeng and Shen, Chunhua and Kong, Tao and Li, Lei}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2021}}@inproceedings{Mao2021pose, title = {{FCPose}: Fully Convolutional Multi-Person Pose Estimation With Dynamic Instance-Aware Convolutions}, author = {Mao, Weian and Tian, Zhi and Wang, Xinlong and Shen, Chunhua}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2021}}LicenseFor academic use, this project is licensed under the 2-clause BSD License - see the LICENSE file for details. For commercial use, please contact Chunhua Shen. { payload :{ allShortcutsEnabled :false, fileTree :{ projects/ABCNet :{ items :[{ name : abcnet, path : projects/ABCNet/abcnet, contentType : directory },{ name ABCNet: An attention-based method for particle tagging.This is the main repository for the ABCNet paper.The implementation uses a modified version of GAPNet to suit the High Energy Physics needs.This repository is divided into two main folders: classification and segmentation, for the quark-gluon tagging and pileup mitigation applications, respectively.The input .h5 files are expected to have the following structure:data: [N,P,F],label:[N,P]pid: [N]global: [N,G]N = Number of eventsF = Number of features per pointP = Number of pointsG = Number of global featuresFor classification, only the pid is required, while for segmentation only label is required.The files to be used for the training (train_files.txt), test (test_files.txt) and evaluation (evaluate_files.txt) are required to be listed in the respective text files.RequirementsTensorflowh5pyClassificationTo train use:cd classificationpython train.py --data_dir ../data/QG/ --log_dir qg_testA logs folder will be created with the training results under the main directory.To evaluate the training use:python evaluate.py --data_dir ../data/QG --model_path ../logs/qg_test --batch 500 --name qg_test --modeln 1SegmentationTo train use:cd segmentationpython train.py --data_dir ../data/PU/ --log_dir pu_testTo evaluate the training use:python evaluate.py --data_dir ../data/PU --model_path ../logs/ou_test --batch 500 --name pu_test LicenseMIT LicenseAcknowledgementsABCNet uses a modified version of GAPNet and PointNet.Comments
Author = {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong}, journal = {IEEE T. Pattern Analysis and Machine Intelligence (TPAMI)}, year = {2021}}@inproceedings{chen2020blendmask, title = {{BlendMask}: Top-Down Meets Bottom-Up for Instance Segmentation}, author = {Chen, Hao and Sun, Kunyang and Tian, Zhi and Shen, Chunhua and Huang, Yongming and Yan, Youliang}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2020}}@inproceedings{zhang2020MEInst, title = {Mask Encoding for Single Shot Instance Segmentation}, author = {Zhang, Rufeng and Tian, Zhi and Shen, Chunhua and You, Mingyu and Yan, Youliang}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2020}}@inproceedings{liu2020abcnet, title = {{ABCNet}: Real-time Scene Text Spotting with Adaptive {B}ezier-Curve Network}, author = {Liu, Yuliang and Chen, Hao and Shen, Chunhua and He, Tong and Jin, Lianwen and Wang, Liangwei}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2020}}@ARTICLE{9525302, author={Liu, Yuliang and Shen, Chunhua and Jin, Lianwen and He, Tong and Chen, Peng and Liu, Chongyu and Chen, Hao}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, title={ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting}, year={2021}, volume={}, number={}, pages={1-1}, doi={10.1109/TPAMI.2021.3107437}} @inproceedings{wang2020solo, title = {{SOLO}: Segmenting Objects by Locations}, author = {Wang, Xinlong and Kong, Tao and Shen, Chunhua and Jiang, Yuning and Li, Lei}, booktitle = {Proc. Eur. Conf. Computer Vision (ECCV)}, year = {2020}}@inproceedings{wang2020solov2, title = {{SOLOv2}: Dynamic and Fast Instance Segmentation}, author = {Wang, Xinlong and Zhang, Rufeng and Kong, Tao and Li, Lei and Shen, Chunhua}, booktitle = {Proc. Advances in Neural Information Processing Systems (NeurIPS)}, year = {2020}}@article{wang2021solo, title = {{SOLO}: A Simple Framework for Instance Segmentation}, author = {Wang, Xinlong and Zhang, Rufeng and Shen, Chunhua and Kong, Tao and Li, Lei}, journal = {IEEE T. Pattern Analysis and Machine Intelligence (TPAMI)}, year = {2021}}@article{tian2019directpose, title = {{DirectPose}: Direct End-to-End Multi-Person Pose Estimation}, author = {Tian, Zhi and Chen, Hao and Shen, Chunhua}, journal = {arXiv preprint arXiv:1911.07451}, year = {2019}}@inproceedings{tian2020conditional, title = {Conditional Convolutions for Instance Segmentation}, author = {Tian, Zhi and Shen, Chunhua and Chen, Hao}, booktitle = {Proc. Eur. Conf. Computer Vision (ECCV)}, year = {2020}}@inproceedings{tian2021boxinst, title = {{BoxInst}: High-Performance Instance Segmentation with Box Annotations}, author = {Tian, Zhi and Shen, Chunhua and Wang, Xinlong and Chen, Hao}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2021}}@inproceedings{wang2021densecl, title = {Dense Contrastive Learning for Self-Supervised Visual Pre-Training}, author = {Wang, Xinlong and Zhang, Rufeng and Shen, Chunhua and Kong, Tao and Li, Lei}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2021}}@inproceedings{Mao2021pose, title = {{FCPose}: Fully Convolutional Multi-Person Pose Estimation With Dynamic Instance-Aware Convolutions}, author = {Mao, Weian and Tian, Zhi and Wang, Xinlong and Shen, Chunhua}, booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, year = {2021}}LicenseFor academic use, this project is licensed under the 2-clause BSD License - see the LICENSE file for details. For commercial use, please contact Chunhua Shen.
2025-04-09ABCNet: An attention-based method for particle tagging.This is the main repository for the ABCNet paper.The implementation uses a modified version of GAPNet to suit the High Energy Physics needs.This repository is divided into two main folders: classification and segmentation, for the quark-gluon tagging and pileup mitigation applications, respectively.The input .h5 files are expected to have the following structure:data: [N,P,F],label:[N,P]pid: [N]global: [N,G]N = Number of eventsF = Number of features per pointP = Number of pointsG = Number of global featuresFor classification, only the pid is required, while for segmentation only label is required.The files to be used for the training (train_files.txt), test (test_files.txt) and evaluation (evaluate_files.txt) are required to be listed in the respective text files.RequirementsTensorflowh5pyClassificationTo train use:cd classificationpython train.py --data_dir ../data/QG/ --log_dir qg_testA logs folder will be created with the training results under the main directory.To evaluate the training use:python evaluate.py --data_dir ../data/QG --model_path ../logs/qg_test --batch 500 --name qg_test --modeln 1SegmentationTo train use:cd segmentationpython train.py --data_dir ../data/PU/ --log_dir pu_testTo evaluate the training use:python evaluate.py --data_dir ../data/PU --model_path ../logs/ou_test --batch 500 --name pu_test LicenseMIT LicenseAcknowledgementsABCNet uses a modified version of GAPNet and PointNet.
2025-04-23Height: 1px; overflow: hidden; position: absolute; white-space: nowrap; width: 1px; margin: 0 !important;} :host { /* scw element */ position: relative; box-sizing: border-box; --margin-right: initial; --margin-bottom: initial; --gap-h-int: initial; --gap-v-int: initial; margin-right: var(--margin-right, var(--gap-h-int, initial)); margin-bottom: var(--margin-bottom, var(--gap-v-int, initial)); display: block;}:host([warning]) { outline: 2px solid red !important;}.scw-link-base { display: block; text-decoration: none;}.scw-link-base:focus-visible { outline: none; box-shadow: var(--scw-effect-focus-shadow); border-radius: 4px;}.scw-link-base:focus:not(:focus-visible) { outline: none; box-shadow: none;}:host .label { display: inline-flex; justify-content: center; align-items: center;}:host([design=main]) { outline: none; background-color: rgba(0, 0, 0, 0); padding-left: var(--scw-spacing-1); padding-right: var(--scw-spacing-1); padding-top: calc(var(--scw-spacing-1) * 0.8); padding-bottom: var(--scw-spacing-1); 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font: var(--scw-type-text-bold-condensed-07-font); color: var(--scw-color-primary-03);}:host([inherit]:hover) .label { font: inherit;}:host([design=main][aria-selected=true]) { background-color: rgba(0, 0, 0, 0); display: flex; flex-flow: column nowrap; justify-content: flex-start; align-items: flex-start; padding-left: var(--scw-spacing-1); padding-right: var(--scw-spacing-1); padding-top: calc(var(--scw-spacing-1) * 0.8); padding-bottom: 0px;}:host([design=main][aria-selected=true]) > *:not(:last-child) { margin-bottom: calc(var(--scw-spacing-1) * 0.8); --margin-bottom: calc( var( --scw-spacing-1 ) * 0.8 );}:host([design=main][aria-selected=true]) .label { text-align: LEFT; font: var(--scw-type-text-bold-condensed-07-font); color: var(--scw-color-grey-03); align-self: stretch; flex: 1;}:host([design=main][aria-selected=true]) .selected { background-color: var(--scw-color-primary-03); align-self: stretch; height: 2px;}:host([inherit] [aria-selected=true]) .label { font: inherit;}:host([design=main][aria-disabled=true]) { background-color: rgba(0, 0, 0, 0); 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font: var(--scw-type-text-font); color: var(--scw-color-grey-02); min-height: 27px;}:host([design=utility]) .icon { width: 24px; height: 24px;}:host([design=utility]) .icon svg { box-sizing: border-box; vertical-align: top; width: 100%; height: 100%; fill: var(--scw-color-grey-02);}:host([design=utility]) .icon svg * { fill: var(--scw-color-grey-02);}:host([design=utility]:focus-visible) { background-color: var(--scw-color-grey-10); border-radius: 4px 4px 4px 4px; box-shadow: var(--scw-effect-focus-shadow);}:host([design=utility]:focus-visible) .label { text-align: LEFT; font: var(--scw-type-text-font); color: var(--scw-color-grey-02);}:host([design=utility]:focus-visible) .icon svg { fill: var(--scw-color-grey-02);}:host([design=utility]:focus-visible) .icon svg * { fill: var(--scw-color-grey-02);}:host([design=utility]:hover) { background-color: rgba(0, 0, 0, 0); cursor: pointer;}:host([design=utility]:hover) .label { text-align: LEFT; font: var(--scw-type-text-font); color: var(--scw-color-primary-03);}:host([design=utility]:hover) .icon svg {
2025-04-04Padding-left: var(--scw-spacing-1); padding-right: var(--scw-spacing-1); padding-top: 0px; padding-bottom: 0px; display: inline-flex;}:host([design=utility]) .scw-link-base > *:not(:last-child) { margin-right: calc(var(--scw-spacing-1) * 0.4); --margin-right: calc( var( --scw-spacing-1 ) * 0.4 );}:host([design=utility]) .label { text-align: LEFT; font: var(--scw-type-text-font); color: var(--scw-color-grey-02); min-height: 27px;}:host([design=utility]) .icon { width: 24px; height: 24px;}:host([design=utility]) .icon svg { box-sizing: border-box; vertical-align: top; width: 100%; height: 100%; fill: var(--scw-color-grey-02);}:host([design=utility]) .icon svg * { fill: var(--scw-color-grey-02);}:host([design=utility]:focus-visible) { background-color: var(--scw-color-grey-10); border-radius: 4px 4px 4px 4px; box-shadow: var(--scw-effect-focus-shadow);}:host([design=utility]:focus-visible) .label { text-align: LEFT; font: var(--scw-type-text-font); color: var(--scw-color-grey-02);}:host([design=utility]:focus-visible) .icon svg { fill: var(--scw-color-grey-02);}:host([design=utility]:focus-visible) .icon svg * { fill: var(--scw-color-grey-02);}:host([design=utility]:hover) { background-color: rgba(0, 0, 0, 0); cursor: pointer;}:host([design=utility]:hover) .label { text-align: LEFT; font: var(--scw-type-text-font); color: var(--scw-color-primary-03);}:host([design=utility]:hover) .icon svg { fill: var(--scw-color-grey-02);}:host([design=utility]:hover) .icon svg * { fill: var(--scw-color-grey-02);}:host([design=utility][aria-selected=true]) { background-color: rgba(0, 0, 0, 0); cursor: pointer;}:host([design=utility][aria-selected=true]) .label { text-align: LEFT; font: var(--scw-type-text-font); color: var(--scw-color-primary-03);}:host([design=utility][aria-selected=true]) .icon svg { fill: var(--scw-color-grey-02);}:host([design=utility][aria-selected=true]) .icon svg * { fill: var(--scw-color-grey-02);}:host([design=utility][aria-disabled=true]) { cursor: auto; pointer-events: none; background-color: rgba(0, 0, 0, 0);}:host([design=utility][aria-disabled=true]) .label { text-align: LEFT; font: var(--scw-type-text-font); color: var(--scw-color-grey-07);}:host([design=utility][aria-disabled=true]) .icon svg { fill: var(--scw-color-grey-02);}:host([design=utility][aria-disabled=true]) .icon svg * { fill: var(--scw-color-grey-02);}:host { white-space: nowrap;}:host .label { white-space: nowrap;} :host([design=plain]) .scw-link-base { outline: none; display: inline-block;} Checking & Savings
2025-04-21/zoom_in ( Take Photo)In the [Program location:] box c:\program files*1\panasonic\pcam\pcam.exeIn the [Program parameter:] box /shutter ( Changing the Settings for Taking Pictures)In the [Program location:] box c:\program files*1\panasonic\pcam\pcam.exeIn the [Program parameter:] box /shutter_name*1 : (64-bit) :Program Files (x86) Click [OK].NOTE- When the Camera Utility is not running, and if you press the buttons to which these functions are assigned, the Camera Utility is activated.- If you operate the Utility after assigning the “Take Photo” function, the same operation will be performed as the one when the check marks both for [Select to save or cancel, after the photo is taken] and [Name the taken photo] are not added ( Changing the Settings for Taking Pictures).- If you operate the Utility after assigning the “Take Photo” function by specifying the file name, the same operation will be performed as the one when the check mark for [Select to save or cancel, after the photo is taken] is added and check mark for [Name the taken photo] is not added ( Changing the Settings for Taking Pictures).- When you assign “Take photo function”or “Take photo function by specifying the file name”to the application buttons, perform the following settings in MCA Configuration Editor. Click [MCA Application] - [Default Handler Configuration] - [Global Settings] - [Camera]. Input “c:\Program Files\Panasonic\PCam\PCam.exe” in [Camera Default Handler]. Input “/shutter” or “/shutter_name” in [CA-LaunchAppCmdLine]. Change [CA-SupportsConcurrentInstances] to [True]. Click [OK].
2025-03-26Fill: var(--scw-color-grey-02);}:host([design=utility]:hover) .icon svg * { fill: var(--scw-color-grey-02);}:host([design=utility][aria-selected=true]) { background-color: rgba(0, 0, 0, 0); cursor: pointer;}:host([design=utility][aria-selected=true]) .label { text-align: LEFT; font: var(--scw-type-text-font); color: var(--scw-color-primary-03);}:host([design=utility][aria-selected=true]) .icon svg { fill: var(--scw-color-grey-02);}:host([design=utility][aria-selected=true]) .icon svg * { fill: var(--scw-color-grey-02);}:host([design=utility][aria-disabled=true]) { cursor: auto; pointer-events: none; background-color: rgba(0, 0, 0, 0);}:host([design=utility][aria-disabled=true]) .label { text-align: LEFT; font: var(--scw-type-text-font); color: var(--scw-color-grey-07);}:host([design=utility][aria-disabled=true]) .icon svg { fill: var(--scw-color-grey-02);}:host([design=utility][aria-disabled=true]) .icon svg * { fill: var(--scw-color-grey-02);}:host { white-space: nowrap;}:host .label { white-space: nowrap;} :host([design=plain]) .scw-link-base { outline: none; display: inline-block;} :host { /* scw element */ position: relative; box-sizing: border-box; --margin-right: initial; --margin-bottom: initial; --gap-h-int: initial; --gap-v-int: initial; margin-right: var(--margin-right, var(--gap-h-int, initial)); margin-bottom: var(--margin-bottom, var(--gap-v-int, initial)); display: block;}:host([warning]) { outline: 2px solid red !important;}:host .responsive { width: 100%; max-width: 100%; height: auto; display: block;} :host { /* scw element */ position: relative; box-sizing: border-box; --margin-right: initial; --margin-bottom: initial; --gap-h-int: initial; --gap-v-int: initial; margin-right: var(--margin-right, var(--gap-h-int, initial)); margin-bottom: var(--margin-bottom, var(--gap-v-int, initial)); display: block;}:host([warning]) { outline: 2px solid red !important;}.scw-link-base { display: block; text-decoration: none;}.scw-link-base:focus-visible { outline: none; box-shadow: var(--scw-effect-focus-shadow); border-radius: 4px;}.scw-link-base:focus:not(:focus-visible) { outline: none; box-shadow: none;}:host .label { display: inline-flex; justify-content: center; align-items: center;}:host([design=main]) { outline: none; background-color: rgba(0, 0, 0, 0); padding-left: var(--scw-spacing-1); padding-right: var(--scw-spacing-1); padding-top: calc(var(--scw-spacing-1) * 0.8); padding-bottom: var(--scw-spacing-1); display: inline-flex;}:host([design=main]) .label { text-align: LEFT; font: var(--scw-type-text-bold-condensed-07-font); color: var(--scw-color-grey-03);}:host([inherit]) .label { font: inherit;}:host([design=main]:focus-visible) { background-color: var(--scw-color-grey-10); border-radius: 4px 4px 4px 4px; box-shadow: var(--scw-effect-focus-shadow); padding-left: var(--scw-spacing-1); padding-right: var(--scw-spacing-1); padding-top: calc(var(--scw-spacing-1) * 0.8); padding-bottom: var(--scw-spacing-1);}:host([design=main]:focus-visible) .label { text-align: LEFT; font: var(--scw-type-text-bold-condensed-07-font); color: var(--scw-color-grey-03);}:host([inherit]:focus-visible) .label { font: inherit;}:host([design=main]:hover) { background-color: rgba(0, 0, 0, 0); padding-left: var(--scw-spacing-1); padding-right: var(--scw-spacing-1); padding-top: calc(var(--scw-spacing-1) * 0.8); padding-bottom: var(--scw-spacing-1); cursor: pointer;}:host([design=main]:hover) .label { text-align: LEFT; font: var(--scw-type-text-bold-condensed-07-font); color: var(--scw-color-primary-03);}:host([inherit]:hover) .label { font: inherit;}:host([design=main][aria-selected=true]) { background-color: rgba(0, 0, 0, 0); display: flex; flex-flow: column nowrap; justify-content: flex-start; align-items: flex-start; padding-left: var(--scw-spacing-1); padding-right: var(--scw-spacing-1); padding-top: calc(var(--scw-spacing-1) * 0.8); padding-bottom: 0px;}:host([design=main][aria-selected=true]) > *:not(:last-child) { margin-bottom: calc(var(--scw-spacing-1) * 0.8); --margin-bottom: calc( var( --scw-spacing-1 ) * 0.8 );}:host([design=main][aria-selected=true]) .label { text-align: LEFT; font: var(--scw-type-text-bold-condensed-07-font); color: var(--scw-color-grey-03); align-self: stretch; flex: 1;}:host([design=main][aria-selected=true]) .selected { background-color: var(--scw-color-primary-03); align-self: stretch; height: 2px;}:host([inherit] [aria-selected=true]) .label { font: inherit;}:host([design=main][aria-disabled=true]) { background-color: rgba(0, 0, 0, 0); padding-left: var(--scw-spacing-1); padding-right: var(--scw-spacing-1); padding-top: calc(var(--scw-spacing-1) * 0.8); padding-bottom: var(--scw-spacing-1); cursor: auto; pointer-events: none;}:host([design=main][aria-disabled=true]) .label { text-align: LEFT; font: var(--scw-type-text-bold-condensed-07-font); color: var(--scw-color-grey-08);}:host([design=main][aria-disabled=true]) .icon svg { fill: var(--scw-color-grey-08);}:host([design=main][aria-disabled=true]) .icon svg * { fill: var(--scw-color-grey-08);}:host([inherit] [aria-disabled=true]) .label { font: inherit;}:host([design=utility]) .scw-link-base { background-color: rgba(0, 0, 0, 0); display: flex; flex-flow: row nowrap; justify-content: flex-start; align-items: center;
2025-04-21