4 Search Results for "Sun, Kevin"

Document
Extended Abstract
DOI: 10.4230/OASIcs.Tokenomics.2021.10
Detecting and Quantifying Crypto Wash Trading (Extended Abstract)

Authors: Lin William Cong, Xi Li, Ke Tang, and Yang Yang

Published in: OASIcs, Volume 97, 3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021)

Abstract
We introduce systematic tests exploiting robust statistical and behavioral patterns in trading to detect fake transactions on 29 cryptocurrency exchanges. Regulated exchanges feature patterns consistently observed in financial markets and nature; abnormal first-significant-digit distributions, size rounding, and transaction tail distributions on unregulated exchanges reveal rampant manipulations unlikely driven by strategy or exchange heterogeneity. We quantify the wash trading on each unregulated exchange, which averaged over 70% of the reported volume. We further document how these fabricated volumes (trillions of dollars annually) improve exchange ranking, temporarily distort prices, and relate to exchange characteristics (e.g., age and userbase), market conditions, and regulation.
Cite as

Lin William Cong, Xi Li, Ke Tang, and Yang Yang. Detecting and Quantifying Crypto Wash Trading (Extended Abstract). In 3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021). Open Access Series in Informatics (OASIcs), Volume 97, pp. 10:1-10:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{cong_et_al:OASIcs.Tokenomics.2021.10,
  author =	{Cong, Lin William and Li, Xi and Tang, Ke and Yang, Yang},
  title =	{{Detecting and Quantifying Crypto Wash Trading}},
  booktitle =	{3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021)},
  pages =	{10:1--10:6},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-220-4},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{97},
  editor =	{Gramoli, Vincent and Halaburda, Hanna and Pass, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.Tokenomics.2021.10},
  URN =		{urn:nbn:de:0030-drops-159072},
  doi =		{10.4230/OASIcs.Tokenomics.2021.10},
  annote =	{Keywords: Bitcoin, Cryptocurrency, FinTech, Forensic Finance, Fraud Detection, Regulation}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2021.45
Sparsification of Directed Graphs via Cut Balance

Authors: Ruoxu Cen, Yu Cheng, Debmalya Panigrahi, and Kevin Sun

Published in: LIPIcs, Volume 198, 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021)

Abstract
In this paper, we consider the problem of designing cut sparsifiers and sketches for directed graphs. To bypass known lower bounds, we allow the sparsifier/sketch to depend on the balance of the input graph, which smoothly interpolates between undirected and directed graphs. We give nearly matching upper and lower bounds for both for-all (cf. Benczúr and Karger, STOC 1996) and for-each (Andoni et al., ITCS 2016) cut sparsifiers/sketches as a function of cut balance, defined the maximum ratio of the cut value in the two directions of a directed graph (Ene et al., STOC 2016). We also show an interesting application of digraph sparsification via cut balance by using it to give a very short proof of a celebrated maximum flow result of Karger and Levine (STOC 2002).
Cite as

Ruoxu Cen, Yu Cheng, Debmalya Panigrahi, and Kevin Sun. Sparsification of Directed Graphs via Cut Balance. In 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 198, pp. 45:1-45:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{cen_et_al:LIPIcs.ICALP.2021.45,
  author =	{Cen, Ruoxu and Cheng, Yu and Panigrahi, Debmalya and Sun, Kevin},
  title =	{{Sparsification of Directed Graphs via Cut Balance}},
  booktitle =	{48th International Colloquium on Automata, Languages, and Programming (ICALP 2021)},
  pages =	{45:1--45:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-195-5},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{198},
  editor =	{Bansal, Nikhil and Merelli, Emanuela and Worrell, James},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2021.45},
  URN =		{urn:nbn:de:0030-drops-141143},
  doi =		{10.4230/LIPIcs.ICALP.2021.45},
  annote =	{Keywords: Graph sparsification, directed graphs, cut sketches, space complexity}
}
Document
DOI: 10.4230/LIPIcs.SoCG.2021.60
A Parallel Batch-Dynamic Data Structure for the Closest Pair Problem

Authors: Yiqiu Wang, Shangdi Yu, Yan Gu, and Julian Shun

Published in: LIPIcs, Volume 189, 37th International Symposium on Computational Geometry (SoCG 2021)

Abstract
We propose a theoretically-efficient and practical parallel batch-dynamic data structure for the closest pair problem. Our solution is based on a serial dynamic closest pair data structure by Golin et al., and supports batches of insertions and deletions in parallel. For a data set of size n, our data structure supports a batch of insertions or deletions of size m in O(m(1+log ((n+m)/m))) expected work and O(log (n+m)log^*(n+m)) depth with high probability, and takes linear space. The key techniques for achieving these bounds are a new work-efficient parallel batch-dynamic binary heap, and careful management of the computation across sets of points to minimize work and depth. We provide an optimized multicore implementation of our data structure using dynamic hash tables, parallel heaps, and dynamic k-d trees. Our experiments on a variety of synthetic and real-world data sets show that it achieves a parallel speedup of up to 38.57x (15.10x on average) on 48 cores with hyper-threading. In addition, we also implement and compare four parallel algorithms for static closest pair problem, for which we are not aware of any existing practical implementations. On 48 cores with hyper-threading, the static algorithms achieve up to 51.45x (29.42x on average) speedup, and Rabin’s algorithm performs the best on average. Comparing our dynamic algorithm to the fastest static algorithm, we find that it is advantageous to use the dynamic algorithm for batch sizes of up to 20% of the data set. As far as we know, our work is the first to experimentally evaluate parallel closest pair algorithms, in both the static and the dynamic settings.
Cite as

Yiqiu Wang, Shangdi Yu, Yan Gu, and Julian Shun. A Parallel Batch-Dynamic Data Structure for the Closest Pair Problem. In 37th International Symposium on Computational Geometry (SoCG 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 189, pp. 60:1-60:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{wang_et_al:LIPIcs.SoCG.2021.60,
  author =	{Wang, Yiqiu and Yu, Shangdi and Gu, Yan and Shun, Julian},
  title =	{{A Parallel Batch-Dynamic Data Structure for the Closest Pair Problem}},
  booktitle =	{37th International Symposium on Computational Geometry (SoCG 2021)},
  pages =	{60:1--60:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-184-9},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{189},
  editor =	{Buchin, Kevin and Colin de Verdi\`{e}re, \'{E}ric},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2021.60},
  URN =		{urn:nbn:de:0030-drops-138594},
  doi =		{10.4230/LIPIcs.SoCG.2021.60},
  annote =	{Keywords: Closest Pair, Parallel Algorithms, Dynamic Algorithms, Experimental Algorithms}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2020.69
Online Algorithms for Weighted Paging with Predictions

Authors: Zhihao Jiang, Debmalya Panigrahi, and Kevin Sun

Published in: LIPIcs, Volume 168, 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)

Abstract
In this paper, we initiate the study of the weighted paging problem with predictions. This continues the recent line of work in online algorithms with predictions, particularly that of Lykouris and Vassilvitski (ICML 2018) and Rohatgi (SODA 2020) on unweighted paging with predictions. We show that unlike unweighted paging, neither a fixed lookahead nor knowledge of the next request for every page is sufficient information for an algorithm to overcome existing lower bounds in weighted paging. However, a combination of the two, which we call the strong per request prediction (SPRP) model, suffices to give a 2-competitive algorithm. We also explore the question of gracefully degrading algorithms with increasing prediction error, and give both upper and lower bounds for a set of natural measures of prediction error.
Cite as

Zhihao Jiang, Debmalya Panigrahi, and Kevin Sun. Online Algorithms for Weighted Paging with Predictions. In 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 168, pp. 69:1-69:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{jiang_et_al:LIPIcs.ICALP.2020.69,
  author =	{Jiang, Zhihao and Panigrahi, Debmalya and Sun, Kevin},
  title =	{{Online Algorithms for Weighted Paging with Predictions}},
  booktitle =	{47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)},
  pages =	{69:1--69:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-138-2},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{168},
  editor =	{Czumaj, Artur and Dawar, Anuj and Merelli, Emanuela},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2020.69},
  URN =		{urn:nbn:de:0030-drops-124761},
  doi =		{10.4230/LIPIcs.ICALP.2020.69},
  annote =	{Keywords: Online algorithms, paging}
}
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