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np2th

Syntologyentry name in harvested coderead from the graph 2026-09-24

np2th appears in the code Syntology harvested for 31 papers, as 2 distinct code bodies found in 32 places (a place is one code body under one paper). At least one of them ran in 31 of the papers; 0 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named np2th do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 2 of the 2 distinct code bodies named np2th; 0 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract

0ran · violated contract

0ran · our draft was wrong

2ran · fixture could not drive it

0ran

0unverified

0fingerprinted

Licence is a property of each copy, so it is counted per place: 12 of the 32 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

31 papers shown of 31, newest first; 32 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive; 1 papers have no page here and are shown by arXiv id only. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Paper	Date	File	Status Syntology	Licence
Learning Where to Edit Vision Transformers	4 Nov 2024	identical code first harvested elsewhere `7d8eaf5537f699e4`	ran · fixture could not drive it	licence of this copy not recorded
Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach	28 Mar 2024	zstarn70/rlrr/models/networks.py `7d8eaf5537f699e4`	ran · fixture could not drive it	no licence file found · pointer only
Towards Optimal Feature-Shaping Methods for Out-of-Distribution Detection	1 Feb 2024	qinyu-allen-zhao/optfsood/model/mlp_mixer.py `7d8eaf5537f699e4`	ran · fixture could not drive it	no licence file found · pointer only
SA$^2$VP: Spatially Aligned-and-Adapted Visual Prompt	16 Dec 2023	identical code first harvested elsewhere `7d8eaf5537f699e4`	ran · fixture could not drive it	licence of this copy not recorded
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning	27 Oct 2023	ubc-tea/SGPT/models/modeling_resnet.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation	24 Oct 2023	SLDGroup/G-CASCADE/lib/networks.py `7d8eaf5537f699e4`	ran · fixture could not drive it	no licence file found · pointer only
FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning	6 Oct 2023	ucsc-vlaa/fedconv/models/modeling.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)
Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments	13 Sep 2023	queyrusi/pelta/ExtendedPelta/Classes/TransformerModels.py `7d8eaf5537f699e4`	ran · fixture could not drive it	no licence file found · pointer only
AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation	7 Aug 2023	jayparanjape/biastuning/vit_seg_modeling.py `7d8eaf5537f699e4`	ran · fixture could not drive it	no licence file found · pointer only
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation	4 Jun 2023	yhygao/UTNet/model/transunet.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)
CLIP-Driven Fine-grained Text-Image Person Re-identification	19 Oct 2022	shuanglinyan/CFine/models/modeling.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)
Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition	25 Sep 2022	kdhht2334/ELIM_FER/common/big_model.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)
TrojViT: Trojan Insertion in Vision Transformers	27 Aug 2022	mxzheng/trojvit/models/modeling.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)
SecretGen: Privacy Recovery on Pre-Trained Models via Distribution Discrimination	25 Jul 2022	AI-secure/SecretGen/tgt_models/vit.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees	6 Jun 2022	VirtuosoResearch/Robust-fine-tuning/exps_on_image_datasets/model/modeling_resnet.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference	15 Apr 2022	hushell/pmf_cvpr22/models/resnet_v2.py `7d8eaf5537f699e4`	ran · fixture could not drive it	Apache-2.0 (permissive)
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?	16 Mar 2022	RICE-EIC/Patch-Fool/models/modeling.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)
MTLDesc: Looking Wider to Describe Better	14 Mar 2022	vignywang/MTLDesc/nets/vit/vit_seg_modeling.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)
CP-ViT: Cascade Vision Transformer Pruning via Progressive Sparsity Prediction	9 Mar 2022	ok858ok/CP-ViT/CP-ViT/models/modeling.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)
Improved Regularization and Robustness for Fine-tuning in Neural Networks	8 Nov 2021	VirtuosoResearch/Regularized-Self-Labeling/model/modeling_resnet.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)
Generalized Radiograph Representation Learning via Cross-supervision between Images and Free-text Radiology Reports	4 Nov 2021	funnyzhou/refers/Pre-train/refers/modules/VisionTransformer.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)
Progressive Coordinate Transforms for Monocular 3D Object Detection	12 Aug 2021	amazon-research/progressive-coordinate-transforms/lib/backbones/modeling_resnet.py `7d8eaf5537f699e4`	ran · fixture could not drive it	Apache-2.0 (permissive)
ResViT: Residual vision transformers for multi-modal medical image synthesis	30 Jun 2021	icon-lab/ResViT/models/residual_transformers.py `7d8eaf5537f699e4`	ran · fixture could not drive it	no licence file found · pointer only
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning	10 Jun 2021	Liangqiong/ViT-FL-main/models/modeling.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT recorded; this copy not marked cleared · pointer only
ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration	13 Apr 2021	junyuchen245/ViT-V-Net_for_3D_Image_Registration_Pytorch/ViT-V-Net/models.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)
On the Robustness of Vision Transformers to Adversarial Examples	31 Mar 2021	MetaMain/ViTRobust/VisionTransformersRobustness/VisionTransformersRobustness/TransformerModels.py `7d8eaf5537f699e4`	ran · fixture could not drive it	BSD-3-Clause (permissive)
ViViT: A Video Vision Transformer	29 Mar 2021	identical code first harvested elsewhere `7d8eaf5537f699e4`	ran · fixture could not drive it	licence of this copy not recorded
TransFG: A Transformer Architecture for Fine-grained Recognition	14 Mar 2021	identical code first harvested elsewhere `7d8eaf5537f699e4`	ran · fixture could not drive it	licence of this copy not recorded
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation	8 Feb 2021	identical code first harvested elsewhere `7d8eaf5537f699e4`	ran · fixture could not drive it	licence of this copy not recorded
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale	22 Oct 2020	jeonsworld/ViT-pytorch/models/modeling.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale	22 Oct 2020	HzcIrving/DeepLearning_PlayGround/VIT/Model.py `749c76cd00ed3a80`	ran · fixture could not drive it	MIT (permissive)
arXiv:Zhu_RCL_Reliable_Continual_Learning_for_Unified_Failure_Detection_CVPR_2024_paper		Impression2805/RCL/model/modeling.py `7d8eaf5537f699e4`	ran · fixture could not drive it	MIT (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

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