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pdist2

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

pdist2 appears in the code Syntology harvested for 10 papers, as 6 distinct code bodies found in 10 places (a place is one code body under one paper). At least one of them ran in 3 of the papers; 1 of the code bodies carries 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 pdist2 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 1 of the 6 distinct code bodies named pdist2; 5 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
1ran · fixture could not drive it
0ran
5unverified
1fingerprinted

Licence is a property of each copy, so it is counted per place: 1 of the 10 places is 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

10 papers shown of 10, newest first; 10 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.

PaperDateFileStatus SyntologyLicence
Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts 1 Jul 2022 multimodallearning/registration-da-mean-teacher/utils.py aa0a8c45bc6ad41c ran · fixture could not drive it fingerprinted MIT (permissive)
Deep learning based geometric registration for medical images: How accurate can we get without visual features? 1 Mar 2021 multimodallearning/deep-geo-reg/utils.py aa0a8c45bc6ad41c ran · fixture could not drive it fingerprinted no licence file found · pointer only
Dense-Resolution Network for Point Cloud Classification and Segmentation 14 May 2020 ShiQiu0419/DRNet/pointnet2/utils/linalg_utils.py 2afefacc6e996a95 unverified MIT (permissive)
Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement 29 Mar 2020 svip-lab/FastMVSNet/fastmvsnet/nn/functional.py af899c701bfaea6b unverified MIT (permissive)
PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds 27 Nov 2019 multimodallearning/Lung250M-4B/registration_models/train_vxmpp_MIND_unsupervised.py aa0a8c45bc6ad41c ran · fixture could not drive it fingerprinted Apache-2.0 (permissive)
DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing 9 Sep 2019 Yochengliu/DensePoint/utils/linalg_utils.py 13065cfadaddfe24 unverified MIT (permissive)
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data 30 Apr 2019 octeufer/Adversarial-Balancing-based-representation-learning-for-Causal-Effect-Inference/ABCEI/evaluation.py 8c8862895d1a2cfc unverified Apache-2.0 (permissive)
Relation-Shape Convolutional Neural Network for Point Cloud Analysis 16 Apr 2019 Yochengliu/Relation-Shape-CNN/utils/linalg_utils.py 13065cfadaddfe24 unverified MIT (permissive)
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks 1 Oct 2018 d909b/perfect_match/perfect_match/models/pehe_loss.py 08f299853fa6e6e0 unverified MIT (permissive)
arXiv:Wang_MLVSNet_Multi-Level_Voting_Siamese_Network_for_3D_Visual_Tracking_ICCV_2021_paper CodeWZT/MLVSNet/pointnet2/utils/linalg_utils.py 2afefacc6e996a95 unverified 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".

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections