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Activation

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

Activation appears in the code Syntology harvested for 28 papers, as 16 distinct code bodies found in 29 places (a place is one code body under one paper). At least one of them ran in 25 of the papers; 3 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 Activation 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 13 of the 16 distinct code bodies named Activation; 3 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
7ran · our draft was wrong
0ran · fixture could not drive it
6ran
3unverified
3fingerprinted

Licence is a property of each copy, so it is counted per place: 10 of the 29 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

28 papers shown of 28, newest first; 29 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, and the graph's for 4 papers added by Syntology; 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
Taming foundation model with invariance-oriented pre-training for broad-spectrum EEG analysis across signal-level, brain-state, and brain-health tasks added by Syntology 2026-08 (from id) jingyingma01/CodeBrain/Models/SSSM.py c6263e54c9b86e6d ran · our draft was wrong Apache-2.0 (permissive)
A Decomposition-based State Space Model for Multivariate Time-Series Forecasting added by Syntology 2026-02 (from id) Neurogica/DecompSSM/models/decompssm/network.py 6893fd503040b43e ran · our draft was wrong BSD-3-Clause-Clear · pointer only
EvoBrain: Dynamic Multi-Channel EEG Graph Modeling for Time-Evolving Brain Networks added by Syntology 2025-09 (from id) Kotoge/EvoBrain/model/s4.py 89b10a8b26bbb7ed ran · our draft was wrong MIT (permissive)
Training a Foundation Model for Materials on a Budget added by Syntology 2025-08 (from id) atomicarchitects/nequix/nequix/torch_impl/model.py b9f7bc58beb62463 unverified MIT (permissive)
Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series 25 Oct 2024 azencot-group/ImagenTime/models/testing_models/s4.py 89b10a8b26bbb7ed ran · our draft was wrong no licence file found · pointer only
ANT: Adaptive Noise Schedule for Time Series Diffusion Models 18 Oct 2024 seunghan96/ANT/src/ANT/arch/s4.py 89b10a8b26bbb7ed ran · our draft was wrong no licence file found · pointer only
Latent Diffusion for Neural Spiking Data 27 Jun 2024 mackelab/LDNS/ldns/networks/blocks.py ae5bb489492e56d4 ran · our draft was wrong MIT (permissive)
The Rise of Diffusion Models in Time-Series Forecasting 5 Jan 2024 ai4healthuol/sssd/src/imputers/CSDIS4.py 89b10a8b26bbb7ed ran · our draft was wrong MIT (permissive)
HOPE: High-order Polynomial Expansion of Black-box Neural Networks 17 Jul 2023 harrypotterxtx/hope/utils/Network.py a6212c5c5a7f9674 ran no licence file found · pointer only
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution 27 Jun 2023 HazyResearch/hyena-dna/src/models/sequence/hyena.py 3cd2f572b4362749 ran · our draft was wrong Apache-2.0 (permissive)
Effectively Modeling Time Series with Simple Discrete State Spaces 16 Mar 2023 hazyresearch/spacetime/model/components.py f51f6c706221f2b0 unverified Apache-2.0 (permissive)
Diffusion-based Conditional ECG Generation with Structured State Space Models 19 Jan 2023 ai4healthuol/sssd-ecg/src/sssd/models/S4Model.py 89b10a8b26bbb7ed ran · our draft was wrong MIT (permissive)
Efficient Movie Scene Detection using State-Space Transformers 29 Dec 2022 md-mohaiminul/trans4mer/trans4mer/model/s4.py 89b10a8b26bbb7ed ran · our draft was wrong Apache-2.0 (permissive)
Efficient Long Sequence Modeling via State Space Augmented Transformer 15 Dec 2022 microsoft/efficientlongsequencemodeling/spade-modules/s4.py 89b10a8b26bbb7ed ran · our draft was wrong MIT (permissive)
Simplifying and Understanding State Space Models with Diagonal Linear RNNs 1 Dec 2022 ag1988/dlr/src/models/sequence/ss/standalone/dss.py a47c8b16e2d50a15 ran · our draft was wrong Apache-2.0 (permissive)
GhostNetV2: Enhance Cheap Operation with Long-Range Attention 23 Nov 2022 likyoo/GhostNetV2-PyTorch/ghostnetv2.py f295958130866a4d ran fingerprinted no licence file found · pointer only
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models 21 Nov 2022 tsy935/graphs4mer/model/s4.py 89b10a8b26bbb7ed ran · our draft was wrong MIT (permissive)
Generating astronomical spectra from photometry with conditional diffusion models 2022-11 (from id) larsdoorenbos/generate-spectra/generative/unet.py 7dbaa0d3af9946c4 ran fingerprinted MIT (permissive)
What Makes Convolutional Models Great on Long Sequence Modeling? 17 Oct 2022 ctlllll/sgconv/gconv_standalone.py 270fa249838a3f14 ran · our draft was wrong no licence file found · pointer only
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting 18 May 2022 tianzhou2011/FiLM/layers/S4.py 89b10a8b26bbb7ed ran · our draft was wrong MIT (permissive)
Long Movie Clip Classification with State-Space Video Models 4 Apr 2022 identical code first harvested elsewhere 89b10a8b26bbb7ed ran · our draft was wrong licence of this copy not recorded
Diagonal State Spaces are as Effective as Structured State Spaces 27 Mar 2022 identical code first harvested elsewhere 89b10a8b26bbb7ed ran · our draft was wrong licence of this copy not recorded
It's Raw! Audio Generation with State-Space Models 20 Feb 2022 identical code first harvested elsewhere 89b10a8b26bbb7ed ran · our draft was wrong licence of this copy not recorded
Efficiently Modeling Long Sequences with Structured State Spaces 31 Oct 2021 raminmh/liquid-s4/src/models/s4/s4.py 89b10a8b26bbb7ed ran · our draft was wrong Apache-2.0 (permissive)
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers 26 Oct 2021 ag1988/dss/src/models/sequence/ss/lssl.py 39a79ffbeaf5161a ran Apache-2.0 (permissive)
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers 26 Oct 2021 hazyresearch/state-spaces/src/models/sequence/modules/lssl.py cb3abb4ada7fc7ff ran Apache-2.0 (permissive)
HiPPO: Recurrent Memory with Optimal Polynomial Projections 17 Aug 2020 identical code first harvested elsewhere 89b10a8b26bbb7ed ran · our draft was wrong licence of this copy not recorded
U-Net: Convolutional Networks for Biomedical Image Segmentation 18 May 2015 zh320/medical-segmentation-pytorch/models/unet.py f3dd5914b236afcb ran fingerprinted Apache-2.0 (permissive)
arXiv:Yang_TINC_Tree-Structured_Implicit_Neural_Compression_CVPR_2023_paper RichealYoung/TINC/utils/Network.py dd39b81c29fff0ab 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".

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