3 Search Results for "Goes, Christopher"


Document
Media Exposition
A Cautionary Tale: Burning the Medial Axis Is Unstable (Media Exposition)

Authors: Erin Chambers, Christopher Fillmore, Elizabeth Stephenson, and Mathijs Wintraecken

Published in: LIPIcs, Volume 224, 38th International Symposium on Computational Geometry (SoCG 2022)


Abstract
The medial axis of a set consists of the points in the ambient space without a unique closest point on the original set. Since its introduction, the medial axis has been used extensively in many applications as a method of computing a topologically equivalent skeleton. Unfortunately, one limiting factor in the use of the medial axis of a smooth manifold is that it is not necessarily topologically stable under small perturbations of the manifold. To counter these instabilities various prunings of the medial axis have been proposed. Here, we examine one type of pruning, called burning. Because of the good experimental results, it was hoped that the burning method of simplifying the medial axis would be stable. In this work we show a simple example that dashes such hopes based on Bing’s house with two rooms, demonstrating an isotopy of a shape where the medial axis goes from collapsible to non-collapsible.

Cite as

Erin Chambers, Christopher Fillmore, Elizabeth Stephenson, and Mathijs Wintraecken. A Cautionary Tale: Burning the Medial Axis Is Unstable (Media Exposition). In 38th International Symposium on Computational Geometry (SoCG 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 224, pp. 66:1-66:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


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@InProceedings{chambers_et_al:LIPIcs.SoCG.2022.66,
  author =	{Chambers, Erin and Fillmore, Christopher and Stephenson, Elizabeth and Wintraecken, Mathijs},
  title =	{{A Cautionary Tale: Burning the Medial Axis Is Unstable}},
  booktitle =	{38th International Symposium on Computational Geometry (SoCG 2022)},
  pages =	{66:1--66:9},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-227-3},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{224},
  editor =	{Goaoc, Xavier and Kerber, Michael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2022.66},
  URN =		{urn:nbn:de:0030-drops-160744},
  doi =		{10.4230/LIPIcs.SoCG.2022.66},
  annote =	{Keywords: Medial axis, Collapse, Pruning, Burning, Stability}
}
Document
F1 Fee Distribution

Authors: Dev Ojha and Christopher Goes

Published in: OASIcs, Volume 71, International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2019)


Abstract
In a proof of stake blockchain, validators need to split the rewards gained from transaction fees each block. Furthermore, these fees must be fairly distributed to each of a validator’s constituent delegators. Delegators accrue this reward throughout the entire time which they are delegated, and they have a special operation to withdraw accrued rewards. The F1 fee distribution scheme works for any algorithm to split fees and inflation between validators each block, with minimal iteration, and the only approximations being due to finite decimal precision. Per block there is a single iteration over the validator set, to enable reward algorithms that differ by validator. No iteration is required to delegate or to withdraw. The state usage is one state update per validator per block and one state entry per active delegation. F1 can optionally handle arbitrary inflation schemes, auto-bonding of rewards, and varying validator commission rates.

Cite as

Dev Ojha and Christopher Goes. F1 Fee Distribution. In International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2019). Open Access Series in Informatics (OASIcs), Volume 71, pp. 10:1-10:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)


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@InProceedings{ojha_et_al:OASIcs.Tokenomics.2019.10,
  author =	{Ojha, Dev and Goes, Christopher},
  title =	{{F1 Fee Distribution}},
  booktitle =	{International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2019)},
  pages =	{10:1--10:6},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-108-5},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{71},
  editor =	{Danos, Vincent and Herlihy, Maurice and Potop-Butucaru, Maria and Prat, Julien and Tucci-Piergiovanni, Sara},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.Tokenomics.2019.10},
  URN =		{urn:nbn:de:0030-drops-119749},
  doi =		{10.4230/OASIcs.Tokenomics.2019.10},
  annote =	{Keywords: Proof of Stake, Fee Distribution, Cosmos}
}
Document
Robust Phoneme Recognition with Little Data

Authors: Christopher Dane Shulby, Martha Dais Ferreira, Rodrigo F. de Mello, and Sandra Maria Aluisio

Published in: OASIcs, Volume 74, 8th Symposium on Languages, Applications and Technologies (SLATE 2019)


Abstract
A common belief in the community is that deep learning requires large datasets to be effective. We show that with careful parameter selection, deep feature extraction can be applied even to small datasets.We also explore exactly how much data is necessary to guarantee learning by convergence analysis and calculating the shattering coefficient for the algorithms used. Another problem is that state-of-the-art results are rarely reproducible because they use proprietary datasets, pretrained networks and/or weight initializations from other larger networks. We present a two-fold novelty for this situation where a carefully designed CNN architecture, together with a knowledge-driven classifier achieves nearly state-of-the-art phoneme recognition results with absolutely no pretraining or external weight initialization. We also beat the best replication study of the state of the art with a 28% FER. More importantly, we are able to achieve transparent, reproducible frame-level accuracy and, additionally, perform a convergence analysis to show the generalization capacity of the model providing statistical evidence that our results are not obtained by chance. Furthermore, we show how algorithms with strong learning guarantees can not only benefit from raw data extraction but contribute with more robust results.

Cite as

Christopher Dane Shulby, Martha Dais Ferreira, Rodrigo F. de Mello, and Sandra Maria Aluisio. Robust Phoneme Recognition with Little Data. In 8th Symposium on Languages, Applications and Technologies (SLATE 2019). Open Access Series in Informatics (OASIcs), Volume 74, pp. 4:1-4:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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@InProceedings{shulby_et_al:OASIcs.SLATE.2019.4,
  author =	{Shulby, Christopher Dane and Ferreira, Martha Dais and de Mello, Rodrigo F. and Aluisio, Sandra Maria},
  title =	{{Robust Phoneme Recognition with Little Data}},
  booktitle =	{8th Symposium on Languages, Applications and Technologies (SLATE 2019)},
  pages =	{4:1--4:11},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-114-6},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{74},
  editor =	{Rodrigues, Ricardo and Janou\v{s}ek, Jan and Ferreira, Lu{\'\i}s and Coheur, Lu{\'\i}sa and Batista, Fernando and Gon\c{c}alo Oliveira, Hugo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2019.4},
  URN =		{urn:nbn:de:0030-drops-108715},
  doi =		{10.4230/OASIcs.SLATE.2019.4},
  annote =	{Keywords: feature extraction, acoustic modeling, phoneme recognition, statistical learning theory}
}
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