863 research outputs found
Callender, Sidney R.
No full textA carte-de-visite print of Sidney R. Callender. He was a Lieutenant in the 3rd Michigan Cavalry
Correspondence, General -- 1943-63 -- Military Service, Sandfly Fever -- letter, 1943-11-06
No full textLetter from Callender, Geo R. to Sabin, Albert B. dated 1943-11-06.Sabin Collection Fair Use Policy</a
Miscellaneous -- 1942 -- Correspondence, Toxoplasmosis -- letter, 1942-07-18
No full textLetter from Callender, Geo R. to Sabin, Albert B. dated 1942-07-18.Sabin Collection Fair Use Policy</a
Correspondence, General -- 1943-63 -- Military Service, Sandfly Fever -- letter, 1943-12-15
No full textLetter from Callender, Geo R. to Sabin, Albert B. dated 1943-12-15.Sabin Collection Fair Use Policy</a
Miscellaneous -- 1942 -- Correspondence, Toxoplasmosis -- letter, 1942-07-27
No full textLetter from Callender, Geo R. to Sabin, Albert B. dated 1942-07-27.Sabin Collection Fair Use Policy</a
Warren, Joel -- 1943-72 -- Correspondence, Individual -- letter, 1943-01-06
No full textLetter from Callender, Geo to Sabin, Albert B. dated 1943-01-06.Sabin Collection Fair Use Policy</a
Expanded Groove MIDI Dataset
No full textThe Expanded Groove MIDI Dataset (E-GMD), a large dataset of human drum performances, with audio recordings annotated in MIDI. E-GMD contains 444 hours of audio from 43 drum kits and is an order of magnitude larger than similar datasets. It is also the first human-performed drum dataset with annotations of velocity.
Additional information is available on the Magenta website: The Expanded Groove MIDI Dataset
If you use the E-GMD dataset in your work, please cite the paper where it was introduced:
Lee Callender, Curtis Hawthorne, and Jesse Engel. "Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset." 2020. arXiv:2004.00188.
You can also use the following BibTeX entry:
@misc{callender2020improving,
title={Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset},
author={Lee Callender and Curtis Hawthorne and Jesse Engel},
year={2020},
eprint={2004.00188},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
Please also make sure to specify which version of the dataset you are using
Implementing Government Policy in Supply Chains: An International Coproduction Study of Public Procurement
No full textPublic procurement is the commercial arm of governments, contracting for goods, and services to feed public sector service provision. However, mainstream operations and supply chain management journals have published little on supply chains to governments, public procurement, and the significance of engaging small businesses in government supply chains. Policy feedback theory and thirteen coproduced international case studies of public procurement and small business agency dyadic relationships are used to explore this space. The research highlights the importance of both public procurement and small business as areas of policy and supply chain management research. Policy feedback theory is introduced as a means to understand relationships and is applied to a coproduction study to understand how supply chain management research can both explore and change policy
Methods for wavelet-based autonomous discrimination of multiple partial discharge sources
No full textRecent years have seen increased interest in the application of on-line condition monitoring of medium voltage networks as the need to maintain and operate ageing cable networks increases. Detection and analysis of partial discharge (PD) activity is generally used as an indicator of pre-breakdown processes that may be indicative of insulation degradation over time. A significant challenge for on-line monitoring is discrimination between multiple partial discharge sources that will often naturally exist in the data. To discriminate between PD sources each PD signal is represented as a feature vector and a clustering algorithm is used to identify clusters in the resulting feature vector space, often after dimensional reduction. Each cluster identified in the data corresponds to a distinct PD source. In this work a comparison of clustering algorithms and dimensional reduction techniques is performed to identify clusters for a variety of PD data sets, in all cases the feature vector is created using wavelet decomposition energies. The three clustering algorithms used were Density Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to Identify Clustering Structure (OPTICS) and Simple Statistics-based Near Neighbour clustering technique (SSNN). The two dimensional reduction techniques considered were Principal Component Analysis (PCA) and t Distributed Stochastic Neighbour Embedding (t SNE). At present the most commonly used combination of dimensional reduction technique and clustering algorithm for PD data are PCA and DBSCAN respectively. From the comparison performed in this work it was found that t SNE combined with OPTICS or SSNN were the most successful at clustering PD data. For use in practical situations SSNN is preferred over OPTICS as it requires only a single input parameter, which generally be hardcoded, leading to a completely autonomous technique. It is therefore suggested that a combination of t SNE and SSNN is particularly appropriate for discriminating PD sources
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