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4 results for “UMAP”
DCASE2021 UAD-S UMAP Data
<p>Support data for our paper:</p> <p><strong>USING UMAP TO INSPECT AUDIO DATA FOR UNSUPERVISED ANOMALY DETECTION UNDER DOMAIN-SHIFT CONDITIONS</strong></p> <p>ArXiv preprint can be found <a href="https://arxiv.org/abs/2107.10880">here</a>. Code for the experiment software pipeline described in the paper can be found <a href="https://github.com/andres-fr/dcase2021_umaps">here</a>. The pipeline requires and generates different forms of data. Here we provide the following:</p> <ol> <li><strong><em>AudioSet_wav_fragments.zip</em></strong>: This is a custom selection of 39437 wav files (32kHz, mono, 10 seconds) randomly extracted from AudioSet (originally released under CC-BY). In addition to this custom subset, the paper also uses the following ones, which can be downloaded at their respective websites: <ol> <li><a href="https://zenodo.org/record/4562016">DCASE2021 Task 2 Development Dataset</a></li> <li><a href="https://zenodo.org/record/4660992">DCASE2021 Task 2 Additional Training Dataset</a></li> <li>Fraunhofer's <a href="https://www.idmt.fraunhofer.de/en/publications/isa-electric-engine.html">IDMT-ISA-ELECTRIC-ENGINE Dataset</a></li> </ol> </li> <li><strong><em>dcase2021_uads_umaps.zip</em></strong>: To compute the UMAPs, first the log-STFT, log-mel and L3 representations must be extracted, and then the UMAPs must be computed. This can take a substantial amount of time and resources. For convenience, we provide here the 72 UMAPs discussed in the paper.</li> <li><strong><em>dcase2021_uads_umap_plots.zip</em></strong>: Also for convenience, we provide here the 198 high-resolution scatter plots rendered from the UMAPs.</li> </ol> <p>For a comprehensive visual inspection of the computed representations, it is sufficient to download the plots only. Users interested in exploring the plots interactively will need to download all the audio datasets and compute the log-STFT, log-mel and L3 representations as well as the UMAPs themselves (code provided in the GitHub repository). UMAPs for further representations can also be computed and plotted.</p>
Mappability of the mouse and human genomes and methylomes with Umap and Bismap
<p>This dataset consists of single-read mappability (Bed files) and multi-read mappability (Wiggle files) of human and mouse genomes and methylomes (bisulfite-converted genome). We provide mappability information for the two most recent assemblies of each organism, and for four different read lengths (24 bp, 36 bp, 50 bp, and 100 bp).</p> <p> </p>
UMAP Embeddings of Sleep-EDF-78
<h1><span dir="auto"><span dir="auto">AISleep: Automated and interpretable sleep staging from single-channel EEG data<br><br></span></span></h1> <p><span lang="EN-US">Xun Mai</span><span lang="EN-US">,</span><span lang="EN-US"> Binghua Song, Manli Luo</span><span lang="EN-US">, Jun Zhu, Xu Jiang, Xiao Ma</span><span lang="EN-US">, Feng Lin</span><span lang="EN-US">, Xiaoqing Hu<sup>5</sup>, Hanchuan Peng<sup>*</sup>, Li Zhang<sup>*</sup>, </span><span lang="EN-US">Y</span><span lang="EN-US">ina Wei<sup>*</sup></span></p> <h2><span lang="EN-US"><sup><br>Abstract</sup></span></h2> <p><span lang="EN-US"><sup><br>Sleep staging is essential for understanding sleep physiology and diagnosing sleep-related disorders. However, traditional manual scoring is time-consuming and resource-intensive, limiting its scalability for large-scale application. In this study, we introduce AISleep, an automated and interpretable unsupervised algorithm based on feature-weighted kernel density estimation (KDE), designed to stage sleep using only a single electroencephalography (EEG) channel. AISleep was evaluated using both public benchmark datasets of healthy subjects and clinical datasets from patients with sleep disorder. It outperforms state-of-the-art (SOTA) unsupervised sleep staging algorithms in young, healthy subjects, and demonstrates better generalizability compared to supervised models. Importantly, we observed that some key EEG features decline with age, which may contribute to reduced staging accuracy in older adults. This study presents a robust and interpretable unsupervised sleep staging algorithm whose lightweight design makes it well-suited for integration into portable devices, offering a practical and scalable solution for accurate, home-based sleep monitoring.<br></sup></span></p> <p> </p> <p><span dir="auto"><span dir="auto"> </span></span></p> <p> </p>
UMAP projections from PIE profiles
UMAP projections of TCGA,POG,MET500 cohorts from PIE profiles
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.